WO2019123633A1 - Acoustic measurement system and parameter generation device - Google Patents

Acoustic measurement system and parameter generation device Download PDF

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Publication number
WO2019123633A1
WO2019123633A1 PCT/JP2017/046138 JP2017046138W WO2019123633A1 WO 2019123633 A1 WO2019123633 A1 WO 2019123633A1 JP 2017046138 W JP2017046138 W JP 2017046138W WO 2019123633 A1 WO2019123633 A1 WO 2019123633A1
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Prior art keywords
sound
unit
time
signal
frequency
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PCT/JP2017/046138
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French (fr)
Japanese (ja)
Inventor
阿部　芳春
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三菱電機株式会社
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Application filed by 三菱電機株式会社 filed Critical 三菱電機株式会社
Priority to PCT/JP2017/046138 priority Critical patent/WO2019123633A1/en
Priority to JP2018526608A priority patent/JP6419392B1/en
Priority to CN201780097594.4A priority patent/CN111480196B/en
Priority to KR1020207016753A priority patent/KR102174553B1/en
Publication of WO2019123633A1 publication Critical patent/WO2019123633A1/en

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B3/00—Applications of devices for indicating or signalling operating conditions of elevators
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K15/00—Acoustics not otherwise provided for
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K15/00—Acoustics not otherwise provided for
    • G10K15/02—Synthesis of acoustic waves
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R29/00—Monitoring arrangements; Testing arrangements

Definitions

  • the present invention relates to an acoustic measurement system that measures the propagation characteristics of sound, and a parameter generation device that generates a parameter for determining whether a grasp target is in a normal state or an abnormal state using this acoustic measurement system.
  • TSP Time Stretched Pulse
  • the present invention was made in order to solve such a problem, and an object of the present invention is to provide an acoustic measurement system capable of accurately estimating a propagation characteristic that changes with time.
  • the sound measurement system includes the sound generator provided at the measurement target, the sound receiver provided at the sound reception point, and one frequency component at each time, and the center frequency of the frequency component changes with time.
  • the sound measurement system has a test signal in which unit signals having one frequency component at each time and the center frequency of the frequency component changes with time are arranged on the time axis from the sound generator to the sound receiver.
  • the propagation characteristics of the sound from the sounding body to the sound receiving body are estimated by propagating between them. This makes it possible to accurately estimate the propagation characteristics that change with time.
  • FIG. 14A is an explanatory diagram in the case where time aggregation processing is not performed on a unit signal array
  • FIG. 14B is a diagram in the case where time aggregation processing is performed.
  • FIG. 14A is an explanatory diagram in the case where time aggregation processing is not performed on a unit signal array
  • FIG. 14B is a diagram in the case where time aggregation processing is performed.
  • FIG. 14A is an explanatory diagram in the case where time aggregation processing is not performed on a unit signal array
  • FIG. 14B is a diagram in the case where time aggregation processing is performed.
  • FIG. 1 is a configuration diagram showing an elevator system as an application example of a parameter generation device according to the present embodiment.
  • the parameter generation device is constituted by a sound sensor 2 mounted on the riding cage 1, a computer 3, and a sounding body 5 provided in the vicinity of the measuring object 4.
  • the riding basket 1 is a riding basket for an elevator, and the sound sensor 2 comprises a microphone.
  • the computer 3 has a USB terminal and a LAN terminal, and the sound sensor 2 is connected to the USB terminal via an audio interface circuit (not shown).
  • a device controlled by the computer 3 is connected to the LAN terminal.
  • the parameter generation device generates, for example, parameters of the abnormal sound diagnosis device for the elevator system as illustrated.
  • the measurement target 4 is a device to be diagnosed in the abnormal sound diagnosis apparatus, and the device to be diagnosed is a device in the hoistway of the elevator.
  • the device to be diagnosed is a device in the hoistway of the elevator.
  • a pulley provided on the top of a rope for driving the riding basket 1, a pulley for supporting the riding basket 1 from below, a cage rail for preventing the cage from rolling, and a weight to balance the weight of the riding cage 1 Counterweights, speed governors for adjusting the basket speed, etc.
  • the sounding body 5 comprises a speaker or the like.
  • FIG. 2 is a configuration diagram of the sound measurement system of the first embodiment and a parameter generation device using the same.
  • the sound measurement system 21 includes a sound sensor 2, a sound generator 5, a measurement unit 6, and an estimation unit 7.
  • the parameter generation device 22 includes a simulated sound synthesis unit 8, a sound source database (sound source DB) 9, a simulation unit 10, and a parameter storage unit 11.
  • the sound sensor 2 is a sound receiver in the sound measurement system 21 and is configured using a microphone.
  • the sounding body 5 is provided in the vicinity of the measurement target 4 and is configured to generate a test sound corresponding to the test signal supplied from the measurement unit 6.
  • FIG. 3 is a block diagram of the sound generator 5.
  • the sounding body 5 includes a control unit 51, a communication interface (communication I / F) 52, and a speaker 53.
  • the control unit 51 includes a microcomputer, performs wireless communication with the measurement unit 6 via the communication interface 52, receives a test signal, drives the speaker 53 based on the received test signal, and controls the output of the test sound.
  • the communication interface 52 includes an interface of a wireless LAN, and has a function of performing communication control with the measurement unit 6.
  • the speaker 53 is a speaker for transmitting a test sound to the air in the hoistway of the elevator.
  • the measurement unit 6 has a function of transmitting a test sound from the sound producing body 5 and acquiring the test sound propagated in the hoistway by the sound sensor 2.
  • the test sound has one frequency component at each time, and unit signals whose center frequency changes with time are arranged on the time axis. is there.
  • a time stretched pulse (TSP) signal can be used as this unit signal.
  • the estimation unit 7 has a function of estimating the propagation characteristics of the sound from the sounding body 5 to the sound sensor 2 based on the relationship between the time and the intensity of the unit signal contained in the test signal.
  • the simulated sound synthesis unit 8 has a function of generating a synthesized simulated sound of an abnormal sound using a sound source stored in the sound source database 9.
  • the simulation unit 10 has a function of determining parameters based on the synthesized simulated sound generated by the simulated sound synthesis unit 8.
  • the parameter storage unit 11 is a storage unit of the parameter determined by the simulation unit 10.
  • the acoustic measurement system and the parameter generation device are configured using the computer 3.
  • the hardware block diagram of the computer 3 is shown in FIG.
  • the computer 3 includes a processor 31, a memory 32, an input / output interface (input / output I / F) 33, and a storage 34.
  • the processor 31 is a processor for realizing the functions of the measurement unit 6, the estimation unit 7, the simulated sound synthesis unit 8 and the simulation unit 10 by executing a program stored in the memory 32 or the storage 34, and has a CPU It is configured using.
  • the memory 32 is a memory including a RAM and the like, which temporarily stores data and the like, and also constitutes a work area of the processor 31.
  • the input / output interface 33 is an interface for exchanging signals between the sound sensor 2 and the sound generator 5 and communicating with other external devices.
  • the storage 34 is a storage unit for storing various data and storing programs corresponding to the respective functions of the measurement unit 6, the estimation unit 7, the simulated sound synthesis unit 8 and the simulation unit 10.
  • the storage 34 also implements the sound source database 9 and the parameter storage unit 11.
  • FIG. 5 is a flowchart showing the operation of the sound measurement system.
  • the measurement unit 6 sends a test signal to the sounding body 5, and causes the sounding body 5 to generate a test sound (step ST1).
  • the sound sensor 2 receives the test sound when the elevator car 1 is operated between the lower floor and the uppermost floor (step ST2) (step ST3). Sent.
  • the test sound received by the sound sensor 2 is sent from the measurement unit 6 to the estimation unit 7, and the estimation unit 7 estimates the propagation characteristic (step ST4), and outputs the propagation characteristic as the estimation result (step ST5) ).
  • FIG. 6 is a flowchart showing the operation of the estimation unit 7.
  • FIG. 7 is an explanatory view of a process of estimating the propagation characteristic from the test signal in the estimation unit 7.
  • the estimation unit 7 performs time-frequency analysis of the received sound wave form, and obtains an intensity distribution S (t, f) on the time axis (frame t) and the frequency axis (frequency bin f) (step ST11).
  • Time-frequency analysis is performed by dividing the sound reception waveform into frames overlapping each other, and determining the intensity for each frequency bin for each frame by FFT (Fast Fourier Transform).
  • a period 71 indicates a unit signal period
  • a spectrogram 72 indicates a spectrogram of a measurement signal.
  • the specific frequency band 73 is a specific frequency band for obtaining signal strength among all the frequencies of each frame.
  • the estimation unit 7 obtains the intensities included in the specific frequency band 73 for each frame t from the intensity distribution S (t, f), and sets this as an intensity time series B (t) (step ST12). Furthermore, the estimation unit 7 detects a peak in the intensity time series B (t) (step ST13). Peak detection is performed by detecting the maximum value of the intensity time series B (t). In FIG. 7, peaks 74a, 74b and 74c indicate the detected peaks.
  • a component other than the unit signal component included in the sound reception signal may be erroneously detected as a peak, so the interval between the detected peaks is measured.
  • step ST14 When the interval between peaks deviates from the unit signal cycle, the peaks are removed (step ST14). The process of step ST14 may be performed as necessary and may be omitted. Finally, the estimation unit 7 extracts the peak value envelope 75 (see FIG. 7) connecting the detected peaks as the intensity of the abnormal sound (step ST15), and outputs the signal of the peak value envelope 75 as the estimated propagation characteristic. (Step ST16).
  • the abnormal sound diagnosis device is that the operation sound of the device is normal or the operation sound of the device for the operation sound when the device is in the normal state and the operation sound when the device is in the abnormal state.
  • an apparatus that determines that there is an abnormality.
  • Such an abnormal sound diagnostic apparatus has, for example, a threshold as a parameter for determining normality and abnormality.
  • the degradation sound diagnosis device for example, the degradation sound diagnosis device, the abnormal point estimation device, and the degradation point estimation device respectively diagnose the degradation sound and set parameters for estimating the abnormal point and the degradation point in the device. Have. These parameters need to be adjusted to be optimal for each device. Therefore, in the present embodiment, synthetic simulated sound is used to design and adjust these parameters. It should be noted that, since samples of abnormal sound and degradation sound are actually difficult to obtain because the frequency of failure of the device is low in many cases, it is necessary to use synthetic simulated sound.
  • the sound sensor 2 makes a normal operation sound when the elevator car 1 is reciprocated between the bottom floor and the top floor (step ST21)
  • the waveform of is acquired (step ST22).
  • the normal operation sound is recorded in the memory 32.
  • the simulated sound synthesis unit 8 selects a sound source from the sound source database 9 (step ST23), controls the intensity of the sound source according to the propagation characteristic changing with the estimated time, and the normal operation sound recorded in the memory 32 And synthesize a plurality of simulated sounds different in abnormal / normal SN ratio (for example, with an SN ratio in the range of 0 to 18 dB in 0.1 dB steps) (step ST24).
  • the SN ratio 0 is assumed to be SN ratio ⁇ , and a normal operation sound having no abnormal sound component is used as a synthetic sound.
  • the simulation unit 10 uses the synthetic simulation sound generated by the simulation sound synthesis unit 8 to find, for example, the relationship between the parameter and the detection rate and the false detection rate in the abnormal sound diagnosis device (step ST26).
  • the detection rate and the false detection rate are as follows.
  • the detection rate is a rate that correctly determines that the operation sound of the device in an abnormal state is abnormal.
  • the false detection rate is a rate that erroneously determines that the operation sound of the device in the normal state is abnormal. In order to accurately obtain the detection rate and the false detection rate, it is necessary to perform simulation using a large number of normal sounds and abnormal sounds.
  • the simulation unit 10 adjusts, for example, a threshold referred to by the abnormal sound diagnosis apparatus as a parameter that affects the detection rate and the false detection rate.
  • the abnormal sound diagnosis apparatus analyzes the operation sound at the time of diagnostic operation, obtains the degree of abnormality, and then compares the degree of abnormality with a threshold to determine the presence or absence of abnormality. Therefore, the threshold is an important parameter that determines the detection rate and the false detection rate, which are the performance of the abnormal sound diagnosis apparatus.
  • the vector representing the threshold is ⁇
  • the vector representing the degree of abnormality is A
  • the abnormality degree vector A is calculated as follows.
  • A (Y- ⁇ ) / ⁇
  • Y is a feature amount vector obtained by analyzing the operation sound of the diagnosis target
  • ⁇ is an average vector thereof
  • ⁇ is a standard deviation vector.
  • ⁇ and ⁇ are averages and standard deviations of feature quantities (feature vectors) X 1 , X 2 ,..., X N (N is the number of normal operation sounds) obtained by analyzing N normal operation sounds It is.
  • the simulation unit 10 obtains a parameter for obtaining the maximum detection rate under the allowable range of the false detection rate as the optimum parameter (step ST27).
  • the threshold value ⁇ [k] of index k is a parameter and taking the value on the horizontal axis and the error rate (0 to 100%) on the vertical axis, the characteristics shown in FIG. 9 are obtained.
  • the (1 ⁇ detection rate) miss rate 91 and the false detection rate is a yield rate 92.
  • the limit value 93 as an allowable range of the false detection rate is provided in the outgoing rate 92.
  • the tapping rate 92 is set to 5% or less.
  • the optimum value of the threshold value ⁇ [k] it can be determined so that the missing rate 91 is minimized under the limit value 93, and in this case, the point ⁇ * [k] in the figure is optimum. It becomes a value.
  • the relationship between S / N and a certain threshold (ath) is determined, and the modulation threshold 101 for finding signs of abnormality is S / N 6 dB
  • the S / N can be determined as a threshold of 6 + ⁇ dB ( ⁇ is, for example, 3 dB).
  • the simulation unit 10 outputs the determined optimum parameter (step ST28), and this is stored in the parameter storage unit 11.
  • the simulation unit 10 learns a sound source position estimation parameter using synthetic simulated sound
  • the sound source position to be estimated by the abnormal point estimation device is, for example, a ride basket, a pit, a counterweight, a top, etc. in an elevator.
  • the sound source position means the installation position in the hoistway of the device where the abnormal sound is generated, that is, the height from the bottom of the hoistway.
  • the abnormal point estimation device estimates the sound source position by referring to the sound source position estimation parameter.
  • the simulation unit 10 optimizes the load and bias of the neural network, which is a sound source position estimation parameter referred to by the abnormal point estimation device, as a parameter affecting the estimation of the sound source position.
  • the abnormality point estimation apparatus obtains an abnormality degree curve which is a change curve of the abnormality degree corresponding to the elevator car position from the abnormality degree vector obtained by analyzing the operation sound at the time of diagnosis, and this abnormality degree The curve is input to the neural network, and the score of the estimated position of the sound source position "Kago", "pit", "counter weight”, "top” score is obtained and the discrimination result with the highest score is output as the estimation result of the sound source position Do.
  • the sound source position estimation parameter of this neural network is composed of a load and a bias, and is learned using synthetic simulated sound whose sound source position is known as teacher data.
  • the sound generator provided at the measurement target, the sound receiver provided at the sound collection point, and one frequency component at each time, the frequency A measurement unit for transmitting a test signal in which unit signals whose center frequency changes with time are arranged on a time axis from a sound producing body to a sound receiving body and acquiring a test signal obtained by the sound receiving body; Since an estimation unit for estimating the propagation characteristics of sound from the sounding body to the sound receiving body based on the relationship between time and intensity of unit signals included in the signal, the propagation characteristics changing with time are accurately determined It can be estimated.
  • the unit signal is a time stretching pulse signal, it is possible to accurately estimate the sound propagation characteristics.
  • the parameter generation device of the first embodiment using the acoustic measurement system of the first embodiment, the parameter for determining whether the measurement target is in the normal state or the abnormal state using the estimated propagation characteristic is used. Since the generation is performed, it is possible to obtain a parameter that can make highly accurate determination even when the propagation characteristic changes with time.
  • a simulation sound synthesis unit that generates a synthesis simulation sound using the propagation characteristics estimated by the estimation unit, and a simulation unit that determines a parameter using a synthesis simulation sound
  • the sound receiving signal includes equipment noise (normal operation noise) and external noise as noise in addition to the test signal component emitted from the sound producing body.
  • equipment noise normal operation noise
  • external noise external noise as noise in addition to the test signal component emitted from the sound producing body.
  • impulsive noise since impulsive noise has its frequency components concentrated in time, it is highly likely to be falsely detected as a peak.
  • an acoustic measurement system will be described in which the influence of impulsive noise on propagation characteristic estimation is eliminated. Since the configuration on the drawing as the sound measurement system and the parameter generation device is the same as the configuration shown in FIG. 2, it will be described using FIG.
  • the estimation unit 7 of the second embodiment is configured to perform frequency analysis of the unit signal, shift the time axis so that the component of the unit signal becomes the same time for each frequency, and obtain the propagation characteristic.
  • the other configurations as the acoustic measurement system and the parameter generation device are the same as in the first embodiment.
  • FIG. 11 is an explanatory drawing showing the arrangement of unit signals in the second embodiment.
  • the array of unit signals 112 is acquired by the estimation unit 7 in unit signal cycles 111.
  • the estimation unit 7 performs time aggregation processing on such unit signal arrays.
  • FIG. 12 is a flowchart showing the operation of the estimation unit 7.
  • the estimation unit 7 performs time frequency analysis on the waveform of the acquired unit signal array, and obtains an intensity distribution S (t, f) on the time axis (frame t) and the frequency axis (frequency bin f) (step ST31).
  • d (fc) fc / (Fs / 2) * Tw
  • Fs is a sampling frequency
  • Tw is a time length (coincident with the period) of the unit signal.
  • a frame shift number nd (fc) obtained by converting the time shift amount d (fc) into a frame number (discrete value) is calculated by the following equation.
  • nd (fc) int (d (fc) /fp+0.5)
  • fp is a frame interval (frame period)
  • int (*) is an integerization function with respect to the argument *
  • 0.5 is a number for reducing the truncation error accompanying the integerization.
  • FIG. 13 is an explanatory view showing the process of the above steps ST31 and ST32.
  • the time-frequency distribution of the original unit signal (unit signal arrangement shown in FIG. 11) is represented as oblique stripes shown in FIG. 13, and the time-aggregated signal after time axis shift is shown as vertical stripes in FIG. Be done. That is, by time-base shifting the unit signal 132 of the unit signal cycle 131, the time-aggregated signal (shifted unit signal) 133 can be obtained.
  • the left-pointing arrows indicate time shift amounts with respect to the original unit signal at each frequency (the time shift amount corresponds to the above calculated nd (fc) assuming that each frequency is fc).
  • the estimation unit 7 obtains an intensity included in a specific frequency band for each frame t, and sets it as an intensity time series B (t) (step ST33) ). Furthermore, the estimation unit 7 detects a peak in the intensity time series B (t) (step ST34). Peak detection is performed by detecting the maximum value of the intensity time series B (t).
  • a component other than the unit signal component included in the sound reception signal may be erroneously detected as a peak, so the interval between the detected peaks is measured. , If the interval between peaks deviates from the unit signal cycle, the peaks are removed (step ST35).
  • step ST35 may be performed as necessary and may be omitted.
  • the estimation unit 7 extracts a peak value envelope connecting the detected peaks (step ST36), corrects the delay of time due to the time shift (step ST37), and outputs it as the estimated propagation characteristic (step ST 38).
  • FIG. 14A shows a time-frequency intensity distribution and a peak value envelope estimated therefrom when impulsive noise (which can be regarded as a disturbance for propagation characteristic estimation) is superimposed on a reception signal.
  • the unit signal component 141a in the sound reception signal appears as oblique stripes, and the impulsive noise (disturbance 142a) appears as vertical stripes.
  • the peak value 145a is higher than the peak value 144a and the peak value 146a due to the influence of the disturbance 142a. Therefore, the peak value envelope 147a is different from the estimation result 148 when there is no disturbance due to the influence of the peak value 145a.
  • FIG. 14B shows a time-frequency distribution obtained by applying a frequency-dependent time shift to the time-frequency distribution of FIG. 14A and a peak value envelope estimated therefrom.
  • the unit signal component 141b after time shift is represented as vertical stripes, and the disturbance 142b after time shift is represented as diagonal stripes.
  • the peak values 144b to 146b which are the intensity of the specific frequency band 143 are not affected by the disturbance 142b after time shift, and the peak value envelope 147b is also close to the estimation result 148 in the absence of the disturbance shown in FIG. It has become.
  • FIGS. 14A and 14B it can be seen that the impact of the impulsive disturbance is eliminated in the estimated propagation characteristics as a result of the frequency-dependent time shift.
  • the estimation unit shifts the time axis for each frequency so that the unit signal has the same time at the same time, the propagation characteristic Therefore, even if there is an impulsive noise, for example, the propagation characteristic can be accurately estimated.
  • the interval between peaks constituting the peak value envelope is the period of the unit signal.
  • the unit signal array has a multiplicity of 2 in order to avoid the complexity of the description.
  • the measuring unit 6 of the third embodiment is configured to use, as a test signal, a unit signal array in which a plurality of unit signals shifted in timing are multiplexed on a time axis. Further, the estimation unit 7 divides the frequency according to the multiplicity of the multiplexed unit signal array, and shifts the time axis for each frequency so that the intensity of the unit signal becomes the same time for each division, and then the propagation characteristic It is configured to ask for The other configurations as the acoustic measurement system and the parameter generation device are the same as in the first embodiment.
  • FIG. 15 is an explanatory drawing showing the arrangement of unit signals in the third embodiment.
  • the estimation unit 7 acquires the arrangement of the unit signals 152 with a unit signal period 151 of multiplicity 2. That is, a unit signal array in which two unit signals 152 are multiplexed in a unit signal cycle 151 is provided.
  • the estimation unit 7 performs time aggregation processing on such unit signal arrays.
  • FIG. 16 is a flowchart showing the operation of the estimation unit 7.
  • the multiplicity is m.
  • the estimation unit 7 performs time-frequency analysis of the received wave form to obtain an intensity distribution S (t, f) on the time axis (frame t) and the frequency axis (frequency bin f) (step ST41).
  • the time shift amount d (fc) is obtained by dividing the entire frequency band into m, finding the index ix of the m divided band to which fc belongs, and calculating according to ix, according to the following equation.
  • m is the multiplicity
  • bw is the bandwidth of the m-divided band
  • ix is the index of the band to which fc belongs
  • Fs is the sampling frequency
  • Tw is the time length (coincident with the period) of the unit signal.
  • a frame shift number nd (fc) obtained by converting the time shift amount d (fc) into a frame number (discrete value) is calculated by the following equation.
  • nd (fc) int (d (fc) /fp+0.5)
  • fp is a frame interval (frame period)
  • int (*) is an integerization function with respect to the argument *
  • FIG. 17 is an explanatory view showing the process of steps ST41 and ST42 described above.
  • the time-frequency distribution of the original unit signal (unit signal arrangement shown in FIG. 15) is represented as oblique stripes shown in FIG. 17, and the time-aggregated signal after time axis shift is shown as vertical stripes in FIG. Be done. That is, by time shifting the unit signal 172 of the unit signal cycle 171 of multiplicity 2 by time axis, the time-aggregated signal of multiplicity 2 (shifted unit signal) 173 can be obtained.
  • the left-pointing arrows indicate time shift amounts with respect to the original unit signal at each frequency (the time shift amount corresponds to the above calculated nd (fc) assuming that each frequency is fc).
  • the estimation unit 7 obtains the intensity included in the specific frequency band b for each frame t from the intensity distribution S ′ (t, f) of the time-aggregated signal and sets it as an intensity time series B (t) (step ST43). Furthermore, the estimation unit 7 detects a peak in the intensity time series B (t) (step ST44). Peak detection is performed by detecting the maximum value of the intensity time series B (t).
  • a component other than the unit signal component included in the sound reception signal may be erroneously detected as a peak, so the interval between the detected peaks is measured. , If the interval between peaks deviates from the unit signal cycle, the peaks are removed (step ST45).
  • step ST45 may be performed as necessary and may be omitted.
  • the estimation unit 7 extracts the peak value envelope connecting the detected peaks (step ST46), corrects the time delay due to the time shift (step ST47), and outputs it as the estimated propagation characteristic (step ST47) ST 48).
  • the time-aggregate signal 173 is obtained by multiplexing.
  • the maximum amount of time shift is reduced to 1 / m by multiplexing, and the overall delay is improved.
  • the sampling interval of the propagation characteristic is 1 / m of the unit signal period 171, and the sampling interval is also improved.
  • FIG. 18 is an explanatory view showing an example of measurement of propagation characteristics by a unit signal arrangement of multiplicity 8; 18A shows a time frequency intensity distribution, FIG. 18B shows a time shift result, FIG. 18C shows a peak detection result, and FIG. 18D shows a peak value envelope (propagation characteristic estimation result).
  • 18A shows a time frequency intensity distribution
  • FIG. 18B shows a time shift result
  • FIG. 18C shows a peak detection result
  • FIG. 18D shows a peak value envelope (propagation characteristic estimation result).
  • the frequency band of 0 to 8000 Hz is shown among the frequency band of 0 to 22050 Hz. Therefore, although the multiplicity appears to be about 2, in practice a unit signal array of multiplicity 8 is used in the band of 0 to 22050 Hz.
  • the horizontal axis represents time (seconds)
  • the vertical axes in FIGS. 18A and 18B represent frequencies (Hz)
  • the vertical axes in FIGS. 18C and 18D represent intensities
  • a disturbance component (vertical stripes) 181 is mixed in the vicinity of 6 seconds. This is dispersed by the time shift dividing the frequency as shown by arrow 183, as shown by disturbance 182 after the time shift in FIG. 18B. Thereby, as shown in FIG. 18C, the peak due to the disturbance is not detected in the peak detection result, and as a result, a good peak value envelope is estimated (see FIG. 18D). Further, since the multiplicity is 8, it is a densely estimated result of 1/8 of the unit signal period in time.
  • the measurement unit uses, as the test signal, a unit signal array in which a plurality of unit signals shifted in timing are multiplexed on the time axis. Therefore, even when the change of the propagation characteristic with respect to time is fast, the propagation characteristic can be measured well.
  • the estimation unit divides the frequency in accordance with the multiplicity of the multiplexed unit signal array, and for each frequency, the frequency of the unit signal is equal at each division. Since the propagation characteristic is determined after shifting the time axis, the propagation characteristic can be accurately estimated even when the change of the propagation characteristic with respect to time is fast.
  • the sound sensor 2 as the sound receiving body is provided at one place of the riding basket 1, but it may be installed at a plurality of places to obtain test sounds from the plurality of sound sensors 2. It is also good.
  • the sounding body 5 is not moved (fixed side) and the sound sensor 2 is moved (moving side), but the present invention is not limited to this.
  • the sound sensor 2 is installed on a signal pole of the intersection to monitor the sound of the accident vehicle, but the present invention is applicable to this as well.
  • the application example to the elevator system has been described in each of the above-described embodiments, in addition to this, the equipment including the moving object such as the vehicle and escalator etc. The same can be applied to the configuration of sound grasping.
  • the present invention allows free combination of each embodiment, or modification of any component of each embodiment, or omission of any component of the embodiment.
  • the acoustic measurement system and the parameter generation device relate to a configuration for obtaining the propagation characteristic when the propagation characteristic changes with time, for example, for use in an abnormal sound diagnosis apparatus for an elevator Is suitable.

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  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
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  • Maintenance And Inspection Apparatuses For Elevators (AREA)

Abstract

A sound generator (5) is provided near equipment (4) to be examined that is an object to be measured. A measurement unit (6) acquires a measurement signal received by a sound receiver (2) by causing a test signal, which is a series of unit signals aligned along a time axis, to propagate from the sound generator (5) to the sound receiver (2), said unit signals having one frequency component for each time with the central frequency in the frequency component changing over time. An estimation unit (7) estimates the propagation properties of sound from the sound generator (5) to the sound receiver (2) on the basis of the relationship between the time and the intensity of the unit signal.

Description

音響計測システム及びパラメータ生成装置Acoustic measurement system and parameter generation device
　この発明は、音の伝播特性を計測する音響計測システムと、この音響計測システムを用いて、把握対象が正常状態か異常状態かを判定するためのパラメータを生成するパラメータ生成装置に関するものである。 The present invention relates to an acoustic measurement system that measures the propagation characteristics of sound, and a parameter generation device that generates a parameter for determining whether a grasp target is in a normal state or an abnormal state using this acoustic measurement system.
　音の伝播特性の計測に関し、例えば特許文献１に示すような装置が知られている。この装置は、スピーカとマイクロホンとの間の音波の伝播時間の測定に、時間引き延ばしパルス（ＴＳＰ：Ｔｉｍｅ　Ｓｔｒｅｔｃｈｅｄ　Ｐｕｌｓｅ）信号を用いたものである。
　なお、音の伝播特性とは、インパルス応答、伝達関数、伝播時間、距離減衰などの総称である。 An apparatus as shown, for example in patent document 1 is known regarding measurement of the propagation characteristic of sound. This device uses a Time Stretched Pulse (TSP) signal to measure the propagation time of the sound wave between the speaker and the microphone.
Note that sound propagation characteristics are a generic term for impulse response, transfer function, propagation time, distance attenuation, and the like.
特開２００４－１９３７８２号公報Japanese Patent Application Laid-Open No. 2004-193782
　上記従来の装置では、スピーカとマイクロホンの設置位置は共に固定であるため、時刻と共に変化する伝播特性を精度良く推定することができないという問題があった。 In the above-mentioned conventional apparatus, since the installation positions of the speaker and the microphone are both fixed, there is a problem that the propagation characteristic changing with time can not be estimated accurately.
　この発明は、かかる問題を解決するためになされたもので、時刻と共に変化する伝播特性を精度良く推定することのできる音響計測システムを提供することを目的とする。 The present invention was made in order to solve such a problem, and an object of the present invention is to provide an acoustic measurement system capable of accurately estimating a propagation characteristic that changes with time.
　この発明に係る音響計測システムは、計測対象に設けた発音体と、受音点に設けた受音体と、各時刻で一つの周波数成分を有し、周波数成分の中心周波数が時間と共に変化する単位信号を時間軸上に配列した試験信号を発音体から受音体までの間を伝播させ、受音体で得られる測定信号を取得する測定部と、測定信号に含まれる単位信号の時間と強度の関係に基づいて、発音体から受音体までの間の音の伝播特性を推定する推定部とを備えたものである。 The sound measurement system according to the present invention includes the sound generator provided at the measurement target, the sound receiver provided at the sound reception point, and one frequency component at each time, and the center frequency of the frequency component changes with time. A measurement unit for transmitting a test signal in which unit signals are arranged on a time axis from a sound producing body to a sound receiving body and acquiring a measurement signal obtained by the sound receiving body, a time of the unit signal included in the measurement signal And an estimation unit for estimating the propagation characteristics of sound from the sounding body to the sound receiving body based on the relationship of strength.
　この発明に係る音響計測システムは、各時刻で一つの周波数成分を有し、周波数成分の中心周波数が時間と共に変化する単位信号を時間軸上に配列した試験信号を発音体から受音体までの間を伝播させることにより、発音体から受音体までの音の伝播特性を推定するようにしたものである。これにより、時刻と共に変化する伝播特性を精度良く推定することができる。 The sound measurement system according to the present invention has a test signal in which unit signals having one frequency component at each time and the center frequency of the frequency component changes with time are arranged on the time axis from the sound generator to the sound receiver. The propagation characteristics of the sound from the sounding body to the sound receiving body are estimated by propagating between them. This makes it possible to accurately estimate the propagation characteristics that change with time.
この発明の実施の形態１の音響計測システムの適用例を示す構成図である。It is a block diagram which shows the application example of the sound measurement system of Embodiment 1 of this invention. この発明の実施の形態１の音響計測システムとパラメータ生成装置を示す構成図である。It is a block diagram which shows the acoustic measurement system of Embodiment 1 of this invention, and a parameter generation apparatus. この発明の実施の形態１の音響計測システムにおける発音体の構成図である。It is a block diagram of the sounding body in the sound measurement system of Embodiment 1 of this invention. この発明の実施の形態１の音響計測システム及びパラメータ生成装置を実現するコンピュータの構成図である。It is a block diagram of the computer which implement | achieves the sound measurement system of Embodiment 1 of this invention, and a parameter generation apparatus. この発明の実施の形態１の音響計測システムの動作を示すフローチャートである。It is a flowchart which shows operation | movement of the sound measurement system of Embodiment 1 of this invention. この発明の実施の形態１の音響計測システムにおける推定部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of the estimation part in the sound measurement system of Embodiment 1 of this invention. この発明の実施の形態１の音響計測システムの推定部における受音信号から伝播特性を推定する処理の説明図である。It is explanatory drawing of the process which estimates a propagation characteristic from the sound receiving signal in the estimation part of the sound measurement system of Embodiment 1 of this invention. この発明の実施の形態１のパラメータ生成装置の動作を示すフローチャートである。It is a flowchart which shows operation | movement of the parameter generation apparatus of Embodiment 1 of this invention. この発明の実施の形態１のパラメータ生成装置における閾値の決定方法を示す説明図である。It is explanatory drawing which shows the determination method of the threshold-value in the parameter generation apparatus of Embodiment 1 of this invention. この発明の実施の形態１のパラメータ生成装置における閾値の決定方法の他の例を示す説明図である。It is explanatory drawing which shows the other example of the determination method of the threshold value in the parameter generation apparatus of Embodiment 1 of this invention. この発明の実施の形態２の音響計測システムにおける単位信号の配列を示す説明図である。It is explanatory drawing which shows arrangement | sequence of the unit signal in the sound measurement system of Embodiment 2 of this invention. この発明の実施の形態２の音響計測システムにおける推定部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of the estimation part in the sound measurement system of Embodiment 2 of this invention. この発明の実施の形態２の音響計測システムにおける時間集約処理を示す説明図である。It is explanatory drawing which shows the time intensive process in the sound measurement system of Embodiment 2 of this invention. 図１４Ａは単位信号配列に対して時間集約処理を行わない場合、図１４Ｂは時間集約処理を行った場合の説明図である。FIG. 14A is an explanatory diagram in the case where time aggregation processing is not performed on a unit signal array, and FIG. 14B is a diagram in the case where time aggregation processing is performed. この発明の実施の形態３の音響計測システムにおける単位信号の配列を示す説明図である。It is explanatory drawing which shows arrangement | sequence of the unit signal in the sound measurement system of Embodiment 3 of this invention. この発明の実施の形態３の音響計測システムにおける推定部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of the estimation part in the sound measurement system of Embodiment 3 of this invention. この発明の実施の形態３の音響計測システムにおける時間集約処理を示す説明図である。It is explanatory drawing which shows the time intensive process in the sound measurement system of Embodiment 3 of this invention. 図１８Ａ～図１８Ｄは、この発明の実施の形態３の音響計測システムにおける多重度８の単位信号配列による伝播特性の計測例を示す説明図である。18A to 18D are explanatory diagrams showing measurement examples of propagation characteristics by unit signal arrangement of multiplicity 8 in the sound measurement system according to the third embodiment of the present invention.
　以下、この発明をより詳細に説明するために、この発明を実施するための形態について、添付の図面に従って説明する。
実施の形態１．
　図１は、本実施の形態によるパラメータ生成装置の適用例としてのエレベータシステムを示す構成図である。
　パラメータ生成装置は、乗車カゴ１の上に搭載した音センサ２とコンピュータ３と計測対象４の近傍に設けた発音体５とにより構成される。乗車カゴ１はエレベータの乗車カゴであり、音センサ２はマイクロホンからなる。コンピュータ３は、ＵＳＢ端子とＬＡＮ端子を備え、ＵＳＢ端子には音センサ２が図示しないオーディオインタフェース回路を介して接続されている。ＬＡＮ端子には、コンピュータ３によって制御される機器が接続されている。パラメータ生成装置は、例えば、図示のようなエレベータシステムに対する異常音診断装置のパラメータを生成する。 Hereinafter, in order to explain the present invention in more detail, a mode for carrying out the present invention will be described according to the attached drawings.
Embodiment 1
FIG. 1 is a configuration diagram showing an elevator system as an application example of a parameter generation device according to the present embodiment.
The parameter generation device is constituted by a sound sensor 2 mounted on the riding cage 1, a computer 3, and a sounding body 5 provided in the vicinity of the measuring object 4. The riding basket 1 is a riding basket for an elevator, and the sound sensor 2 comprises a microphone. The computer 3 has a USB terminal and a LAN terminal, and the sound sensor 2 is connected to the USB terminal via an audio interface circuit (not shown). A device controlled by the computer 3 is connected to the LAN terminal. The parameter generation device generates, for example, parameters of the abnormal sound diagnosis device for the elevator system as illustrated.
　計測対象４は異常音診断装置における診断対象機器であり、この診断対象機器としては、エレベータの昇降路内にある機器である。例えば、乗車カゴ１を駆動するロープの頂部に設けられる滑車や乗車カゴ１を下から支えるための滑車、また、カゴが横揺れしないようにするためのカゴレール、乗車カゴ１の重量とバランスするためのカウンターウエイト、カゴ速度を調整するための調速機等がある。発音体５はスピーカ等からなる。 The measurement target 4 is a device to be diagnosed in the abnormal sound diagnosis apparatus, and the device to be diagnosed is a device in the hoistway of the elevator. For example, a pulley provided on the top of a rope for driving the riding basket 1, a pulley for supporting the riding basket 1 from below, a cage rail for preventing the cage from rolling, and a weight to balance the weight of the riding cage 1 Counterweights, speed governors for adjusting the basket speed, etc. The sounding body 5 comprises a speaker or the like.
　図２は、実施の形態１の音響計測システム及びこれを用いたパラメータ生成装置の構成図である。音響計測システム２１は、音センサ２、発音体５、測定部６、推定部７からなる。また、パラメータ生成装置２２として、模擬音合成部８、音源データベース（音源ＤＢ）９、シミュレーション部１０、パラメータ記憶部１１を備えている。音センサ２は音響計測システム２１における受音体であり、マイクロホンを用いて構成されている。発音体５は計測対象４の近傍に設けられ、測定部６から与えられる試験信号に対応した試験音を発生するよう構成されている。 FIG. 2 is a configuration diagram of the sound measurement system of the first embodiment and a parameter generation device using the same. The sound measurement system 21 includes a sound sensor 2, a sound generator 5, a measurement unit 6, and an estimation unit 7. Further, the parameter generation device 22 includes a simulated sound synthesis unit 8, a sound source database (sound source DB) 9, a simulation unit 10, and a parameter storage unit 11. The sound sensor 2 is a sound receiver in the sound measurement system 21 and is configured using a microphone. The sounding body 5 is provided in the vicinity of the measurement target 4 and is configured to generate a test sound corresponding to the test signal supplied from the measurement unit 6.
　図３は発音体５の構成図である。図示のように、発音体５は、制御部５１、通信インタフェース（通信Ｉ／Ｆ）５２、スピーカ５３からなる。制御部５１はマイクロコンピュータからなり、通信インタフェース５２を介して測定部６との無線通信を行って試験信号を受信すると共に、受信した試験信号に基づいてスピーカ５３を駆動し、試験音の出力制御を行う機能を有している。通信インタフェース５２は無線ＬＡＮのインタフェースを備え、測定部６との通信制御を行う機能を有している。スピーカ５３はエレベータの昇降路内の空中に試験音を送出するためのスピーカである。 FIG. 3 is a block diagram of the sound generator 5. As illustrated, the sounding body 5 includes a control unit 51, a communication interface (communication I / F) 52, and a speaker 53. The control unit 51 includes a microcomputer, performs wireless communication with the measurement unit 6 via the communication interface 52, receives a test signal, drives the speaker 53 based on the received test signal, and controls the output of the test sound. Have the ability to The communication interface 52 includes an interface of a wireless LAN, and has a function of performing communication control with the measurement unit 6. The speaker 53 is a speaker for transmitting a test sound to the air in the hoistway of the elevator.
　図２に戻り、測定部６は、試験音を発音体５から送出させ、昇降路内を伝播した試験音を音センサ２で取得する機能を有している。ここで、試験音としては、後述する図７で示すように、各時刻で一つの周波数成分を有し、この周波数成分の中心周波数が時間と共に変化する単位信号を時間軸上に配列したものである。この単位信号としては時間引き延ばしパルス（ＴＳＰ）信号を用いることができる。
　推定部７は、試験信号に含まれる単位信号の時間と強度の関係に基づいて、発音体５から音センサ２までの間の音の伝播特性を推定する機能を有している。模擬音合成部８は、音源データベース９に格納されている音源を用いて異常音の合成模擬音を生成する機能を有している。シミュレーション部１０は、模擬音合成部８で生成された合成模擬音に基づいてパラメータを決定する機能を有している。パラメータ記憶部１１は、シミュレーション部１０で決定されたパラメータの記憶部である。 Returning to FIG. 2, the measurement unit 6 has a function of transmitting a test sound from the sound producing body 5 and acquiring the test sound propagated in the hoistway by the sound sensor 2. Here, as shown in FIG. 7 to be described later, the test sound has one frequency component at each time, and unit signals whose center frequency changes with time are arranged on the time axis. is there. A time stretched pulse (TSP) signal can be used as this unit signal.
The estimation unit 7 has a function of estimating the propagation characteristics of the sound from the sounding body 5 to the sound sensor 2 based on the relationship between the time and the intensity of the unit signal contained in the test signal. The simulated sound synthesis unit 8 has a function of generating a synthesized simulated sound of an abnormal sound using a sound source stored in the sound source database 9. The simulation unit 10 has a function of determining parameters based on the synthesized simulated sound generated by the simulated sound synthesis unit 8. The parameter storage unit 11 is a storage unit of the parameter determined by the simulation unit 10.
　これら音響計測システム及びパラメータ生成装置は、コンピュータ３を用いて構成されている。図４にコンピュータ３のハードウェア構成図を示す。図示のように、コンピュータ３は、プロセッサ３１、メモリ３２、入出力インタフェース（入出力Ｉ／Ｆ）３３、ストレージ３４からなる。プロセッサ３１は、メモリ３２またはストレージ３４に記憶されたプログラムを実行することにより、測定部６、推定部７、模擬音合成部８及びシミュレーション部１０の機能を実現するためのプロセッサであり、ＣＰＵを用いて構成されている。メモリ３２は、ＲＡＭ等からなり、データ等の一時記憶を行うと共に、プロセッサ３１の作業領域を構成するメモリである。入出力インタフェース３３は、音センサ２と発音体５との信号授受及びその他の外部装置との通信を行うためのインタフェースである。ストレージ３４は、各種のデータを格納すると共に、測定部６、推定部７、模擬音合成部８及びシミュレーション部１０のそれぞれの機能に対応したプログラムを格納するための記憶部である。また、ストレージ３４は、音源データベース９及びパラメータ記憶部１１を実現している。 The acoustic measurement system and the parameter generation device are configured using the computer 3. The hardware block diagram of the computer 3 is shown in FIG. As illustrated, the computer 3 includes a processor 31, a memory 32, an input / output interface (input / output I / F) 33, and a storage 34. The processor 31 is a processor for realizing the functions of the measurement unit 6, the estimation unit 7, the simulated sound synthesis unit 8 and the simulation unit 10 by executing a program stored in the memory 32 or the storage 34, and has a CPU It is configured using. The memory 32 is a memory including a RAM and the like, which temporarily stores data and the like, and also constitutes a work area of the processor 31. The input / output interface 33 is an interface for exchanging signals between the sound sensor 2 and the sound generator 5 and communicating with other external devices. The storage 34 is a storage unit for storing various data and storing programs corresponding to the respective functions of the measurement unit 6, the estimation unit 7, the simulated sound synthesis unit 8 and the simulation unit 10. The storage 34 also implements the sound source database 9 and the parameter storage unit 11.
　次に、実施の形態１の音響計測システムとパラメータ生成装置の動作について説明する。
　図５は、音響計測システムの動作を示すフローチャートである。
　先ず測定部６は、発音体５に対して試験信号を送出し、発音体５から試験音を発生させる（ステップＳＴ１）。次に、音センサ２は、エレベータの乗車カゴ１を最下階と最上階との間で往復運転させた場合（ステップＳＴ２）の試験音を受音し（ステップＳＴ３）、これが測定部６に送られる。音センサ２で受音された試験音は測定部６から推定部７に送出され、推定部７では、伝播特性を推定し（ステップＳＴ４）、その推定結果である伝播特性を出力する（ステップＳＴ５）。 Next, operations of the sound measurement system and the parameter generation device according to the first embodiment will be described.
FIG. 5 is a flowchart showing the operation of the sound measurement system.
First, the measurement unit 6 sends a test signal to the sounding body 5, and causes the sounding body 5 to generate a test sound (step ST1). Next, the sound sensor 2 receives the test sound when the elevator car 1 is operated between the lower floor and the uppermost floor (step ST2) (step ST3). Sent. The test sound received by the sound sensor 2 is sent from the measurement unit 6 to the estimation unit 7, and the estimation unit 7 estimates the propagation characteristic (step ST4), and outputs the propagation characteristic as the estimation result (step ST5) ).
　図６は推定部７の動作を示すフローチャートである。図７は推定部７における試験信号から伝播特性を推定する処理の説明図である。
　推定部７は、先ず、受音波形を時間周波数分析し、時間軸（フレームｔ）と周波数軸（周波数ビンｆ）に関する強度分布Ｓ（ｔ，ｆ）を求める（ステップＳＴ１１）。時間周波数分析は、受音波形を互いにオーバーラップするフレームに分割し、各フレームに対し、ＦＦＴ（高速フーリエ変換）により、周波数ビン毎の強度を求めることで行う。図７において、周期７１は単位信号周期を示し、スペクトログラム７２は測定信号のスペクトログラムを示している。また、特定周波数帯域７３は、各フレームの全周波数のうち、信号強度を求めるための特定の周波数帯域である。 FIG. 6 is a flowchart showing the operation of the estimation unit 7. FIG. 7 is an explanatory view of a process of estimating the propagation characteristic from the test signal in the estimation unit 7.
First, the estimation unit 7 performs time-frequency analysis of the received sound wave form, and obtains an intensity distribution S (t, f) on the time axis (frame t) and the frequency axis (frequency bin f) (step ST11). Time-frequency analysis is performed by dividing the sound reception waveform into frames overlapping each other, and determining the intensity for each frequency bin for each frame by FFT (Fast Fourier Transform). In FIG. 7, a period 71 indicates a unit signal period, and a spectrogram 72 indicates a spectrogram of a measurement signal. The specific frequency band 73 is a specific frequency band for obtaining signal strength among all the frequencies of each frame.
　次に、推定部７は、強度分布Ｓ（ｔ，ｆ）から、フレームｔ毎に特定周波数帯域７３に含まれる強度を求め、これを強度時系列Ｂ（ｔ）とする（ステップＳＴ１２）。さらに、推定部７は強度時系列Ｂ（ｔ）においてピークを検出する（ステップＳＴ１３）。ピーク検出は強度時系列Ｂ（ｔ）の極大値を検出することで実行する。図７において、ピーク７４ａ，７４ｂ，７４ｃは検出したピークを示している。ここで、強度時系列Ｂ（ｔ）から、ピークを検出する際、受音信号に含まれる単位信号成分以外の成分をピークとして誤検出することがあるため、検出されたピーク間の間隔を計り、ピーク間の間隔が単位信号周期から外れる場合、そのピークを除去する（ステップＳＴ１４）なお、このステップＳＴ１４の処理は必要に応じて行い、省略することも可能である。
　最後に、推定部７は検出したピークを結ぶピーク値包絡線７５（図７参照）を異常音の強度として抽出し（ステップＳＴ１５）、ピーク値包絡線７５の信号を推定した伝播特性として出力する（ステップＳＴ１６）。 Next, the estimation unit 7 obtains the intensities included in the specific frequency band 73 for each frame t from the intensity distribution S (t, f), and sets this as an intensity time series B (t) (step ST12). Furthermore, the estimation unit 7 detects a peak in the intensity time series B (t) (step ST13). Peak detection is performed by detecting the maximum value of the intensity time series B (t). In FIG. 7, peaks 74a, 74b and 74c indicate the detected peaks. Here, when detecting a peak from the intensity time series B (t), a component other than the unit signal component included in the sound reception signal may be erroneously detected as a peak, so the interval between the detected peaks is measured. When the interval between peaks deviates from the unit signal cycle, the peaks are removed (step ST14). The process of step ST14 may be performed as necessary and may be omitted.
Finally, the estimation unit 7 extracts the peak value envelope 75 (see FIG. 7) connecting the detected peaks as the intensity of the abnormal sound (step ST15), and outputs the signal of the peak value envelope 75 as the estimated propagation characteristic. (Step ST16).
　次に、パラメータ生成装置２２の動作について図８のフローチャートに沿って説明する。なお、以下の説明では、音響計測システム２１によって伝播特性２１ａが求められており、パラメータ生成装置２２の模擬音合成部８はこれを取得済みであるとする。
　ここで、本実施の形態で生成するパラメータとは、次のようなものである。
　異常音診断装置は、機器が正常状態にあるときの作動音と、機器が異常状態にあるときの作動音に対して、それぞれ、機器の作動音が正常であること、あるいは、機器の作動音が異常であることを判定する装置である。このような異常音診断装置では、正常と異常を判定するためのパラメータとして、例えば、閾値を有している。
　異常音診断装置以外にも、例えば、劣化音診断装置、異常個所推定装置、劣化個所推定装置においても、それぞれ、劣化音を診断し、異常個所、劣化個所を推定するためのパラメータを装置内に有している。これらのパラメータはそれぞれの装置にとって最適となるよう調整されることが必要である。そこで、本実施の形態では、これらのパラメータを設計及び調整するために合成模擬音を用いる。なお、異常音や劣化音のサンプルは、実際には、機器の故障頻度が少なく取得が困難であることが多いため、合成模擬音を用いる必要がある。 Next, the operation of the parameter generation device 22 will be described along the flowchart of FIG. In the following description, it is assumed that the propagation characteristic 21a is obtained by the acoustic measurement system 21, and the simulated sound synthesis unit 8 of the parameter generation device 22 has already acquired it.
Here, the parameters generated in the present embodiment are as follows.
The abnormal sound diagnosis device is that the operation sound of the device is normal or the operation sound of the device for the operation sound when the device is in the normal state and the operation sound when the device is in the abnormal state. Is an apparatus that determines that there is an abnormality. Such an abnormal sound diagnostic apparatus has, for example, a threshold as a parameter for determining normality and abnormality.
In addition to the abnormal sound diagnosis device, for example, the degradation sound diagnosis device, the abnormal point estimation device, and the degradation point estimation device respectively diagnose the degradation sound and set parameters for estimating the abnormal point and the degradation point in the device. Have. These parameters need to be adjusted to be optimal for each device. Therefore, in the present embodiment, synthetic simulated sound is used to design and adjust these parameters. It should be noted that, since samples of abnormal sound and degradation sound are actually difficult to obtain because the frequency of failure of the device is low in many cases, it is necessary to use synthetic simulated sound.
　先ず、発音体５からの試験音の発音をオフにした状態で、音センサ２は、エレベータの乗車カゴ１を最下階と最上階の間で往復させた場合（ステップＳＴ２１）の正常動作音の波形を取得する（ステップＳＴ２２）。この正常動作音はメモリ３２に記録される。次に、模擬音合成部８は、音源データベース９から音源を選択し（ステップＳＴ２３）、推定された時刻と共に変化する伝播特性に従い、音源の強度を制御し、メモリ３２に記録された正常動作音に重畳し、異常／正常のＳＮ比の異なる模擬音を（例えば、０．１ｄＢステップで０から１８ｄＢまでの範囲のＳＮ比で）複数個合成し（ステップＳＴ２４）、シミュレーション部１０に合成模擬音として出力する（ステップＳＴ２５）。なお、ＳＮ比０は、便宜上、ＳＮ比－∞とし、異常音成分のない正常動作音を合成音とするものとする。 First, in a state in which the sound generation of the test sound from the sounding body 5 is turned off, the sound sensor 2 makes a normal operation sound when the elevator car 1 is reciprocated between the bottom floor and the top floor (step ST21) The waveform of is acquired (step ST22). The normal operation sound is recorded in the memory 32. Next, the simulated sound synthesis unit 8 selects a sound source from the sound source database 9 (step ST23), controls the intensity of the sound source according to the propagation characteristic changing with the estimated time, and the normal operation sound recorded in the memory 32 And synthesize a plurality of simulated sounds different in abnormal / normal SN ratio (for example, with an SN ratio in the range of 0 to 18 dB in 0.1 dB steps) (step ST24). Output as (step ST25). For the sake of convenience, the SN ratio 0 is assumed to be SN ratio −∞, and a normal operation sound having no abnormal sound component is used as a synthetic sound.
　次に、シミュレーション部１０は、模擬音合成部８で生成した合成模擬音を用いて、例えば、異常音診断装置におけるパラメータと検出率及び誤検出率の関係を求める（ステップＳＴ２６）。ここで、検出率及び誤検出率とは次の通りである。検出率は、異常状態にある機器の作動音を異常と正しく判定する率である。一方、誤検出率は、正常状態にある機器の作動音を異常と誤って判定する率である。なお、検出率と誤検出率を精度よく求めるため、多数の正常音と異常音を用いてシミュレーションを行う必要がある。
　シミュレーション部１０は、検出率及び誤検出率に影響を与えるパラメータとして、例えば、異常音診断装置が参照する閾値を調整する。異常音診断装置は、診断運転時の作動音を分析し、異常度を取得した後、異常度を閾値と比較し、異常の有無を判定する。従って、閾値は、異常音診断装置の性能である検出率と誤検出率を決定づける重要なパラメータである。
　いま、閾値を表すベクトルをθ、異常度を表すベクトルをＡ、両者のベクトルの要素を指すインデックスをｋ（ｋ＝０，１，２，…，Ｋ、Ｋは次元数）とすると、シミュレーション部１０は、あるｋに対してＡ［ｋ］＞Θ［ｋ］が成立つならば異常と判定し、そうでなければ、正常と判定する（下式参照）。

Figure JPOXMLDOC01-appb-I000001
　ここで、異常度ベクトルＡは、次式のように計算される。
　　Ａ＝（Ｙ－μ）／σ
　また、Ｙは診断対象の作動音を分析して得られる特徴量ベクトル、μはその平均ベクトル、σは標準偏差ベクトルである。μとσは、正常時の作動音Ｎ個を分析して得られる特徴量（特徴ベクトル）Ｘ１，Ｘ２，…，ＸＮ（Ｎは正常時の作動音の個数）の平均と標準偏差である。 Next, the simulation unit 10 uses the synthetic simulation sound generated by the simulation sound synthesis unit 8 to find, for example, the relationship between the parameter and the detection rate and the false detection rate in the abnormal sound diagnosis device (step ST26). Here, the detection rate and the false detection rate are as follows. The detection rate is a rate that correctly determines that the operation sound of the device in an abnormal state is abnormal. On the other hand, the false detection rate is a rate that erroneously determines that the operation sound of the device in the normal state is abnormal. In order to accurately obtain the detection rate and the false detection rate, it is necessary to perform simulation using a large number of normal sounds and abnormal sounds.
The simulation unit 10 adjusts, for example, a threshold referred to by the abnormal sound diagnosis apparatus as a parameter that affects the detection rate and the false detection rate. The abnormal sound diagnosis apparatus analyzes the operation sound at the time of diagnostic operation, obtains the degree of abnormality, and then compares the degree of abnormality with a threshold to determine the presence or absence of abnormality. Therefore, the threshold is an important parameter that determines the detection rate and the false detection rate, which are the performance of the abnormal sound diagnosis apparatus.
Now, assuming that the vector representing the threshold is θ, the vector representing the degree of abnormality is A, and the indices indicating the elements of both vectors are k (k = 0, 1, 2,..., K, K is the number of dimensions) 10 determines that it is abnormal if A [k]> Θ [k] holds for a certain k, and otherwise determines that it is normal (see the following equation).

Figure JPOXMLDOC01-appb-I000001
Here, the abnormality degree vector A is calculated as follows.
A = (Y-μ) / σ
Further, Y is a feature amount vector obtained by analyzing the operation sound of the diagnosis target, μ is an average vector thereof, and σ is a standard deviation vector. μ and σ are averages and standard deviations of feature quantities (feature vectors) X 1 , X 2 ,..., X N (N is the number of normal operation sounds) obtained by analyzing N normal operation sounds It is.
　次に、シミュレーション部１０は、誤検出率の許容範囲下で最大検出率を得るパラメータを最適パラメータとして求める（ステップＳＴ２７）。いま、インデックスｋの閾値θ［ｋ］をパラメータとして、その値を横軸にとり、誤り率（０～１００％）を縦軸にすると、図９に示すような特性が得られる。ここで、（１－検出率）を見逃し率９１、誤検出率を湧き出し率９２とする。閾値θ［ｋ］が大きい程、湧き出し率９２は低下するのに対して、見逃し率９１は増加する。また、湧き出し率９２には、誤検出率の許容範囲としての制限値９３が設けられている。制限値９３としては、例えば、Ｓ／Ｎが６ｄＢの時、湧き出し率９２を５％以下などとする。
　閾値θ［ｋ］の最適値を決める一つの方法として、制限値９３の下で、見逃し率９１が最小となるように決めることができ、この場合、図中の点θ＊［ｋ］が最適値となる。 Next, the simulation unit 10 obtains a parameter for obtaining the maximum detection rate under the allowable range of the false detection rate as the optimum parameter (step ST27). Now, taking the threshold value θ [k] of index k as a parameter and taking the value on the horizontal axis and the error rate (0 to 100%) on the vertical axis, the characteristics shown in FIG. 9 are obtained. Here, it is assumed that the (1−detection rate) miss rate 91 and the false detection rate is a yield rate 92. As the threshold value θ [k] is larger, the overrun rate 92 decreases while the oversight rate 91 increases. In addition, the limit value 93 as an allowable range of the false detection rate is provided in the outgoing rate 92. As the limit value 93, for example, when the S / N is 6 dB, the tapping rate 92 is set to 5% or less.
As one method of determining the optimum value of the threshold value θ [k], it can be determined so that the missing rate 91 is minimized under the limit value 93, and in this case, the point θ * [k] in the figure is optimum. It becomes a value.
　また、図１０に示すように、Ｓ／Ｎを変えた合成模擬音を用いて、Ｓ／Ｎとある閾値（ａｔｈ）の関係を求め、異常の兆しを見つけるための変調閾値１０１をＳ／Ｎ６ｄＢの閾値とし、異常判定のための異常閾値１０２をＳ／Ｎが６＋αｄＢ（αは例えば３ｄＢ）の閾値と決めるなどとすることができる。 Also, as shown in FIG. 10, using a synthetic simulated sound with different S / N, the relationship between S / N and a certain threshold (ath) is determined, and the modulation threshold 101 for finding signs of abnormality is S / N 6 dB The S / N can be determined as a threshold of 6 + α dB (α is, for example, 3 dB).
　その後、シミュレーション部１０は、求めた最適パラメータを出力し（ステップＳＴ２８）、これがパラメータ記憶部１１に記憶される。 Thereafter, the simulation unit 10 outputs the determined optimum parameter (step ST28), and this is stored in the parameter storage unit 11.
　次に、シミュレーション部１０が、合成模擬音を用いて音源位置推定パラメータを学習する例について説明する。
　例えば、パラメータ生成装置を異常個所推定装置に適用した場合、異常箇所推定装置で推定すべき音源位置としては、例えば、エレベータにおける、乗車カゴ、ピット、カウンターウエイト、頂部などである。ここで、音源位置は異常音が発生している機器の昇降路内の設置位置、すなわち昇降路底面からの高さを意味する。異常個所推定装置は、音源位置推定パラメータを参照することにより音源位置を推定する。そこで、シミュレーション部１０は、音源位置の推定に影響を与えるパラメータとして、異常個所推定装置が参照する音源位置推定パラメータであるニューラルネットワークの荷重とバイアスを最適化する。
　その一例としては、異常個所推定装置は、診断時の作動音を分析して得られる異常度ベクトルから、エレベータのカゴ位置に対応する異常度の変化曲線である異常度曲線を求め、この異常度曲線をニューラルネットワークに入力し、音源位置の推定スコア「カゴ」、「ピット」、「カウンターウエイト」、「頂部」のスコアを得て、最大のスコアを有する識別結果を音源位置の推定結果として出力する。このニューラルネットワークの音源位置推定パラメータは、荷重とバイアスとからなり、音源位置が既知の合成模擬音を教師データとして用いて学習されたものである。 Next, an example in which the simulation unit 10 learns a sound source position estimation parameter using synthetic simulated sound will be described.
For example, when the parameter generation device is applied to the abnormal point estimation device, the sound source position to be estimated by the abnormal point estimation device is, for example, a ride basket, a pit, a counterweight, a top, etc. in an elevator. Here, the sound source position means the installation position in the hoistway of the device where the abnormal sound is generated, that is, the height from the bottom of the hoistway. The abnormal point estimation device estimates the sound source position by referring to the sound source position estimation parameter. Therefore, the simulation unit 10 optimizes the load and bias of the neural network, which is a sound source position estimation parameter referred to by the abnormal point estimation device, as a parameter affecting the estimation of the sound source position.
As an example, the abnormality point estimation apparatus obtains an abnormality degree curve which is a change curve of the abnormality degree corresponding to the elevator car position from the abnormality degree vector obtained by analyzing the operation sound at the time of diagnosis, and this abnormality degree The curve is input to the neural network, and the score of the estimated position of the sound source position "Kago", "pit", "counter weight", "top" score is obtained and the discrimination result with the highest score is output as the estimation result of the sound source position Do. The sound source position estimation parameter of this neural network is composed of a load and a bias, and is learned using synthetic simulated sound whose sound source position is known as teacher data.
　以上説明したように、実施の形態１の音響計測システムによれば、計測対象に設けた発音体と、受音点に設けた受音体と、各時刻で一つの周波数成分を有し、周波数成分の中心周波数が時間と共に変化する単位信号を時間軸上に配列した試験信号を発音体から受音体までの間を伝播させ、受音体で得られる試験信号を取得する測定部と、試験信号に含まれる単位信号の時間と強度の関係に基づいて、発音体から受音体までの間の音の伝播特性を推定する推定部とを備えたので、時刻と共に変化する伝播特性を精度良く推定することができる。 As described above, according to the sound measurement system of the first embodiment, the sound generator provided at the measurement target, the sound receiver provided at the sound collection point, and one frequency component at each time, the frequency A measurement unit for transmitting a test signal in which unit signals whose center frequency changes with time are arranged on a time axis from a sound producing body to a sound receiving body and acquiring a test signal obtained by the sound receiving body; Since an estimation unit for estimating the propagation characteristics of sound from the sounding body to the sound receiving body based on the relationship between time and intensity of unit signals included in the signal, the propagation characteristics changing with time are accurately determined It can be estimated.
　また、実施の形態１の音響計測システムによれば、単位信号を、時間引き延ばしパルス信号としたので、音の伝播特性を精度良く推定することができる。 Further, according to the sound measurement system of the first embodiment, since the unit signal is a time stretching pulse signal, it is possible to accurately estimate the sound propagation characteristics.
　また、実施の形態１のパラメータ生成装置によれば、実施の形態１の音響計測システムを用い、推定された伝播特性を用いて、計測対象が正常状態か異常状態かを判定するためのパラメータを生成するようにしたので、時刻と共に伝播特性が変化する場合でも精度の高い判定を行うことができるパラメータを得ることができる。 Further, according to the parameter generation device of the first embodiment, using the acoustic measurement system of the first embodiment, the parameter for determining whether the measurement target is in the normal state or the abnormal state using the estimated propagation characteristic is used. Since the generation is performed, it is possible to obtain a parameter that can make highly accurate determination even when the propagation characteristic changes with time.
　また、実施の形態１のパラメータ生成装置によれば、推定部で推定された伝播特性を用いて合成模擬音を生成する模擬音合成部と、合成模擬音を用いてパラメータを決定するシミュレーション部とを備えたので、さらに精度の高い判定を行うことのできるパラメータを得ることができる。 Further, according to the parameter generation device of the first embodiment, a simulation sound synthesis unit that generates a synthesis simulation sound using the propagation characteristics estimated by the estimation unit, and a simulation unit that determines a parameter using a synthesis simulation sound Thus, it is possible to obtain parameters that can make a more accurate determination.
実施の形態２．
　受音信号は、発音体から発した試験信号成分の他に、機器騒音（正常動作音）や外部騒音を騒音として含んでいる。特に、衝撃性の騒音は、その周波数成分が時間的に集中するため、ピークとして誤検出される可能性が高い。そこで、実施の形態２では、衝撃性の騒音による伝播特性推定に与える影響を除去するようにした音響計測システムを説明する。音響計測システム及びパラメータ生成装置としての図面上の構成は図２に示した構成と同様であるため、図２を用いて説明する。 Second Embodiment
The sound receiving signal includes equipment noise (normal operation noise) and external noise as noise in addition to the test signal component emitted from the sound producing body. In particular, since impulsive noise has its frequency components concentrated in time, it is highly likely to be falsely detected as a peak. Thus, in the second embodiment, an acoustic measurement system will be described in which the influence of impulsive noise on propagation characteristic estimation is eliminated. Since the configuration on the drawing as the sound measurement system and the parameter generation device is the same as the configuration shown in FIG. 2, it will be described using FIG.
　実施の形態２の推定部７は、単位信号を周波数分析し、周波数毎に単位信号の成分が同一時刻になるよう時間軸をシフトさせた上で伝播特性を求めるよう構成されている。その他の音響計測システム及びパラメータ生成装置としての構成は実施の形態１と同様である。 The estimation unit 7 of the second embodiment is configured to perform frequency analysis of the unit signal, shift the time axis so that the component of the unit signal becomes the same time for each frequency, and obtain the propagation characteristic. The other configurations as the acoustic measurement system and the parameter generation device are the same as in the first embodiment.
　次に、実施の形態２の動作について説明する。
　図１１は、実施の形態２における単位信号の配列を示す説明図である。図示のように、単位信号周期１１１で単位信号１１２の配列が推定部７で取得される。推定部７はこのような単位信号配列に対して時間集約処理を行う。図１２は推定部７の動作を示すフローチャートである。 Next, the operation of the second embodiment will be described.
FIG. 11 is an explanatory drawing showing the arrangement of unit signals in the second embodiment. As illustrated, the array of unit signals 112 is acquired by the estimation unit 7 in unit signal cycles 111. The estimation unit 7 performs time aggregation processing on such unit signal arrays. FIG. 12 is a flowchart showing the operation of the estimation unit 7.
　推定部７は、先ず、取得した単位信号配列の波形を時間周波数分析し、時間軸（フレームｔ）と周波数軸（周波数ビンｆ）に関する強度分布Ｓ（ｔ，ｆ）を求める（ステップＳＴ３１）。次に、強度分布Ｓ（ｔ，ｆ）の各周波数ビンｆの成分に対して、周波数ｆに応じた時間シフト量ｄ（ｆ）だけ時間軸をシフトした強度分布Ｓ’（ｔ，ｆ）を求める（ステップＳＴ３２）。ここで、周波数ｆ＝ｆｃに対する時間シフト量ｄ（ｆｃ）は次式で計算される。
　　ｄ（ｆｃ）＝ｆｃ／（Ｆｓ／２）＊Ｔｗ
　ここで、Ｆｓはサンプリング周波数、Ｔｗは単位信号の時間長（周期と一致）である。また、時間シフト量ｄ（ｆｃ）をフレーム数（離散値）に換算したフレームシフト数ｎｄ（ｆｃ）は次式で計算される。
　　ｎｄ（ｆｃ）＝ｉｎｔ（ｄ（ｆｃ）／ｆｐ＋０．５）
　ここで、ｆｐはフレーム間隔（フレーム周期）、ｉｎｔ（＊）は引数＊に対する整数化関数、０．５は整数化に伴う打切り誤差を削減するための数である。
　従って、時間軸をシフトした強度分布Ｓ’（ｔ，ｆ＝ｆｃ）は、次式で計算される。
　　Ｓ’（ｔ，ｆ＝ｆｃ）＝Ｓ（ｔ＋ｎｄ（ｆｃ），ｆ＝ｆｃ） First, the estimation unit 7 performs time frequency analysis on the waveform of the acquired unit signal array, and obtains an intensity distribution S (t, f) on the time axis (frame t) and the frequency axis (frequency bin f) (step ST31). Next, with respect to the components of each frequency bin f of the intensity distribution S (t, f), an intensity distribution S ′ (t, f) in which the time axis is shifted by the time shift amount d (f) according to the frequency f It asks for (step ST32). Here, the time shift amount d (fc) for the frequency f = fc is calculated by the following equation.
d (fc) = fc / (Fs / 2) * Tw
Here, Fs is a sampling frequency, and Tw is a time length (coincident with the period) of the unit signal. Further, a frame shift number nd (fc) obtained by converting the time shift amount d (fc) into a frame number (discrete value) is calculated by the following equation.
nd (fc) = int (d (fc) /fp+0.5)
Here, fp is a frame interval (frame period), int (*) is an integerization function with respect to the argument *, and 0.5 is a number for reducing the truncation error accompanying the integerization.
Therefore, the intensity distribution S '(t, f = fc) shifted with the time axis is calculated by the following equation.
S '(t, f = fc) = S (t + nd (fc), f = fc)
　図１３は、以上のステップＳＴ３１、ＳＴ３２の処理を示す説明図である。
　元の単位信号の時間周波数分布（図１１に示す単位信号配列）は、図１３に示される斜めの縞として表され、時間軸シフト後の時間集約信号は、図１３中の縦の縞として表される。すなわち、単位信号周期１３１の単位信号１３２を時間軸シフトすることで、時間集約信号（シフト後単位信号）１３３が求められる。ここで、左向きの矢印はそれぞれの周波数における元の単位信号に対する時間シフト量（時間シフト量は、それぞれの周波数をｆｃとすると上述の計算されたｎｄ（ｆｃ）に対応）を示す。 FIG. 13 is an explanatory view showing the process of the above steps ST31 and ST32.
The time-frequency distribution of the original unit signal (unit signal arrangement shown in FIG. 11) is represented as oblique stripes shown in FIG. 13, and the time-aggregated signal after time axis shift is shown as vertical stripes in FIG. Be done. That is, by time-base shifting the unit signal 132 of the unit signal cycle 131, the time-aggregated signal (shifted unit signal) 133 can be obtained. Here, the left-pointing arrows indicate time shift amounts with respect to the original unit signal at each frequency (the time shift amount corresponds to the above calculated nd (fc) assuming that each frequency is fc).
　次に、推定部７は、時間集約信号の強度分布Ｓ’（ｔ，ｆ）から、フレームｔ毎に特定の周波数帯域に含まれる強度を求め、強度時系列Ｂ（ｔ）とする（ステップＳＴ３３）。さらに、推定部７は、強度時系列Ｂ（ｔ）においてピークを検出する（ステップＳＴ３４）。ピーク検出は強度時系列Ｂ（ｔ）の極大値を検出することで実行する。ここで、強度時系列Ｂ（ｔ）から、ピークを検出する際、受音信号に含まれる単位信号成分以外の成分をピークとして誤検出することがあるため、検出されたピーク間の間隔を計り、ピーク間の間隔が単位信号周期から外れる場合、そのピークを除去する（ステップＳＴ３５）。なお、このステップＳＴ３５の処理は、必要に応じて行い、省略することも可能である。
　最後に、推定部７は、検出したピークを結ぶピーク値包絡線を抽出し（ステップＳＴ３６）、時間シフトによる時刻の遅れを補正して（ステップＳＴ３７）、推定した伝播特性として、出力する（ステップＳＴ３８）。 Next, from the intensity distribution S ′ (t, f) of the time-aggregated signal, the estimation unit 7 obtains an intensity included in a specific frequency band for each frame t, and sets it as an intensity time series B (t) (step ST33) ). Furthermore, the estimation unit 7 detects a peak in the intensity time series B (t) (step ST34). Peak detection is performed by detecting the maximum value of the intensity time series B (t). Here, when detecting a peak from the intensity time series B (t), a component other than the unit signal component included in the sound reception signal may be erroneously detected as a peak, so the interval between the detected peaks is measured. , If the interval between peaks deviates from the unit signal cycle, the peaks are removed (step ST35). The process of step ST35 may be performed as necessary and may be omitted.
Finally, the estimation unit 7 extracts a peak value envelope connecting the detected peaks (step ST36), corrects the delay of time due to the time shift (step ST37), and outputs it as the estimated propagation characteristic (step ST 38).
　図１４Ａは、受音信号に、衝撃性の騒音（伝播特性推定に対する外乱とみなせる）が重畳した場合の時間周波数強度分布とそこから推定されるピーク値包絡線を示す。受音信号中の単位信号成分１４１ａは斜めの縞として、衝撃性の騒音（外乱１４２ａ）は縦縞として現れる。これにより、特定周波数帯域１４３における強度としてピーク値１４４ａ～１４６ａのうち、ピーク値１４５ａは外乱１４２ａの影響によりピーク値１４４ａとピーク値１４６ａに比べて高い値となっている。従って、ピーク値包絡線１４７ａは、ピーク値１４５ａの影響により外乱が無いときの推定結果１４８とは異なるものになっている。 FIG. 14A shows a time-frequency intensity distribution and a peak value envelope estimated therefrom when impulsive noise (which can be regarded as a disturbance for propagation characteristic estimation) is superimposed on a reception signal. The unit signal component 141a in the sound reception signal appears as oblique stripes, and the impulsive noise (disturbance 142a) appears as vertical stripes. As a result, among the peak values 144a to 146a as the intensity in the specific frequency band 143, the peak value 145a is higher than the peak value 144a and the peak value 146a due to the influence of the disturbance 142a. Therefore, the peak value envelope 147a is different from the estimation result 148 when there is no disturbance due to the influence of the peak value 145a.
　図１４Ｂは、図１４Ａの時間周波数分布に対して周波数に依存する時間シフトを適用して得られる時間周波数分布とそこから推定されるピーク値包絡線を示す。時間シフト後の単位信号成分１４１ｂは縦縞として表され、時間シフト後の外乱１４２ｂは斜めの縞として表されている。これにより、特定周波数帯域１４３の強度であるピーク値１４４ｂ～１４６ｂでも時間シフト後の外乱１４２ｂの影響はなく、ピーク値包絡線１４７ｂも図１４Ａで示す外乱が無いときの推定結果１４８に近いものとなっている。
　このように、図１４Ａと図１４Ｂを比較すると、周波数に依存する時間シフトの結果、推定される伝播特性において、衝撃性の外乱の影響が除去されることが分かる。 FIG. 14B shows a time-frequency distribution obtained by applying a frequency-dependent time shift to the time-frequency distribution of FIG. 14A and a peak value envelope estimated therefrom. The unit signal component 141b after time shift is represented as vertical stripes, and the disturbance 142b after time shift is represented as diagonal stripes. As a result, even the peak values 144b to 146b which are the intensity of the specific frequency band 143 are not affected by the disturbance 142b after time shift, and the peak value envelope 147b is also close to the estimation result 148 in the absence of the disturbance shown in FIG. It has become.
Thus, comparing FIGS. 14A and 14B, it can be seen that the impact of the impulsive disturbance is eliminated in the estimated propagation characteristics as a result of the frequency-dependent time shift.
　以上説明したように、実施の形態２の音響計測システムによれば、推定部は、単位信号を、単位信号の強度が同一時刻になるよう、周波数毎に時間軸をシフトさせた上で伝播特性を求めるようにしたので、例えば衝撃性の騒音があった場合でも伝播特性を精度良く推定することができる。 As described above, according to the sound measurement system of the second embodiment, the estimation unit shifts the time axis for each frequency so that the unit signal has the same time at the same time, the propagation characteristic Therefore, even if there is an impulsive noise, for example, the propagation characteristic can be accurately estimated.
実施の形態３．
　多重度１の単位信号配列を用いる実施の形態１、２では、ピーク値包絡線を構成するピークの間隔は、単位信号の周期となる。伝播特性の時刻に対する変化が速い場合、単位信号の周期よりも時間的に短い周期で伝播特性を計測する必要がある。そこで、実施の形態３として、伝播特性の時刻に対する変化が速い場合でも、伝播特性を良好に計測することができるようにした音響計測システムを説明する。なお、本実施の形態では、説明の煩雑さを避けるため、単位信号配列の多重度を２とした場合を説明するが、多重度が３以上、例えば８といった値でも適用可能である。音響計測システム及びパラメータ生成装置としての図面上の構成は図２に示した構成と同様であるため、図２を用いて説明する。 Third Embodiment
In the first and second embodiments in which unit signal arrays of multiplicity 1 are used, the interval between peaks constituting the peak value envelope is the period of the unit signal. When the change of the propagation characteristic with respect to time is fast, it is necessary to measure the propagation characteristic with a period shorter in time than the period of the unit signal. Therefore, as the third embodiment, an acoustic measurement system will be described in which the propagation characteristic can be measured favorably even when the change of the propagation characteristic with respect to time is fast. In the present embodiment, the unit signal array has a multiplicity of 2 in order to avoid the complexity of the description. However, it is also possible to use a value having a multiplicity of three or more, for example, eight. Since the configuration on the drawing as the sound measurement system and the parameter generation device is the same as the configuration shown in FIG. 2, it will be described using FIG.
　実施の形態３の測定部６は、試験信号として、それぞれタイミングをずらした複数の単位信号を時間軸上で多重した単位信号配列を用いるよう構成されている。また、推定部７は、多重した単位信号配列の多重度に応じて周波数を分割し、分割毎に、単位信号の強度が同一時刻になるよう周波数毎に時間軸をシフトさせた上で伝播特性を求めるよう構成されている。その他の音響計測システム及びパラメータ生成装置としての構成は実施の形態１と同様である。 The measuring unit 6 of the third embodiment is configured to use, as a test signal, a unit signal array in which a plurality of unit signals shifted in timing are multiplexed on a time axis. Further, the estimation unit 7 divides the frequency according to the multiplicity of the multiplexed unit signal array, and shifts the time axis for each frequency so that the intensity of the unit signal becomes the same time for each division, and then the propagation characteristic It is configured to ask for The other configurations as the acoustic measurement system and the parameter generation device are the same as in the first embodiment.
　次に、実施の形態３の動作について説明する。
　図１５は、実施の形態３における単位信号の配列を示す説明図である。図示のように、多重度２の単位信号周期１５１で単位信号１５２の配列が推定部７で取得される。すなわち、単位信号周期１５１中に単位信号１５２が２個多重されている単位信号配列となっている。推定部７はこのような単位信号配列に対して時間集約処理を行う。図１６は推定部７の動作を示すフローチャートである。ここでは多重度をｍとしている。 Next, the operation of the third embodiment will be described.
FIG. 15 is an explanatory drawing showing the arrangement of unit signals in the third embodiment. As illustrated, the estimation unit 7 acquires the arrangement of the unit signals 152 with a unit signal period 151 of multiplicity 2. That is, a unit signal array in which two unit signals 152 are multiplexed in a unit signal cycle 151 is provided. The estimation unit 7 performs time aggregation processing on such unit signal arrays. FIG. 16 is a flowchart showing the operation of the estimation unit 7. Here, the multiplicity is m.
　推定部７は、先ず、受音波形を時間周波数分析し、時間軸（フレームｔ）と周波数軸（周波数ビンｆ）に関する強度分布Ｓ（ｔ，ｆ）を求める（ステップＳＴ４１）。次に、強度分布Ｓ（ｔ，ｆ）の各周波数ビンｆの成分に対して、周波数ｆに応じた時間シフト量ｄ（ｆ）だけ時間軸をシフトした強度分布Ｓ’（ｔ，ｆ）を求める（ステップＳＴ４２）。ここで、周波数ｆ＝ｆｃに対する時間シフト量ｄ（ｆｃ）は次式で計算される。
　時間シフト量ｄ（ｆｃ）は、全周波数帯域をｍ分割し、ｆｃが属するｍ分割帯域のインデックスｉｘを求め、ｉｘに応じて、次式のように計算する。
　　ｂｗ＝（Ｆｓ／２）／ｍ
　　ｉｘ＝ｉｎｔ（ｆｃ／ｂｗ）
　　ｄ（ｆｃ）＝（ｆｃ－ｂｗ＊ｉｘ）／（Ｆｓ／２）＊Ｔｗ
　ここで、ｍは多重度、ｂｗはｍ分割した帯域の帯域幅、ｉｘはｆｃが属する帯域のインデックス、Ｆｓはサンプリング周波数、Ｔｗは単位信号の時間長（周期と一致）である。 First, the estimation unit 7 performs time-frequency analysis of the received wave form to obtain an intensity distribution S (t, f) on the time axis (frame t) and the frequency axis (frequency bin f) (step ST41). Next, with respect to the components of each frequency bin f of the intensity distribution S (t, f), an intensity distribution S ′ (t, f) in which the time axis is shifted by the time shift amount d (f) according to the frequency f It asks for (step ST42). Here, the time shift amount d (fc) for the frequency f = fc is calculated by the following equation.
The time shift amount d (fc) is obtained by dividing the entire frequency band into m, finding the index ix of the m divided band to which fc belongs, and calculating according to ix, according to the following equation.
bw = (Fs / 2) / m
ix = int (fc / bw)
d (fc) = (fc-bw * ix) / (Fs / 2) * Tw
Here, m is the multiplicity, bw is the bandwidth of the m-divided band, ix is the index of the band to which fc belongs, Fs is the sampling frequency, and Tw is the time length (coincident with the period) of the unit signal.
　また、時間シフト量ｄ（ｆｃ）をフレーム数（離散値）に換算したフレームシフト数ｎｄ（ｆｃ）は次式で計算される。
　　ｎｄ（ｆｃ）＝ｉｎｔ（ｄ（ｆｃ）／ｆｐ＋０．５）
　ここで、ｆｐはフレーム間隔（フレーム周期）、ｉｎｔ（＊）は引数＊に対する整数化関数、０．５は整数化に伴う打切り誤差を削減するための数である。
　従って、時間軸をシフトした強度分布Ｓ’（ｔ，ｆ＝ｆｃ）は、次式で計算される。
　　Ｓ’（ｔ，ｆ＝ｆｃ）＝Ｓ（ｔ＋ｎｄ（ｆｃ），ｆ＝ｆｃ） Further, a frame shift number nd (fc) obtained by converting the time shift amount d (fc) into a frame number (discrete value) is calculated by the following equation.
nd (fc) = int (d (fc) /fp+0.5)
Here, fp is a frame interval (frame period), int (*) is an integerization function with respect to the argument *, and 0.5 is a number for reducing the truncation error accompanying the integerization.
Therefore, the intensity distribution S '(t, f = fc) shifted with the time axis is calculated by the following equation.
S '(t, f = fc) = S (t + nd (fc), f = fc)
　図１７は、以上のステップＳＴ４１、ＳＴ４２の処理を示す説明図である。
　元の単位信号の時間周波数分布（図１５に示す単位信号配列）は、図１７に示される斜めの縞として表され、時間軸シフト後の時間集約信号は、図１７中の縦の縞として表される。すなわち、多重度２の単位信号周期１７１の単位信号１７２を時間軸シフトすることで、多重度２の時間集約信号（シフト後単位信号）１７３が求められる。ここで、左向きの矢印はそれぞれの周波数における元の単位信号に対する時間シフト量（時間シフト量は、それぞれの周波数をｆｃとすると上述の計算されたｎｄ（ｆｃ）に対応）を示す。 FIG. 17 is an explanatory view showing the process of steps ST41 and ST42 described above.
The time-frequency distribution of the original unit signal (unit signal arrangement shown in FIG. 15) is represented as oblique stripes shown in FIG. 17, and the time-aggregated signal after time axis shift is shown as vertical stripes in FIG. Be done. That is, by time shifting the unit signal 172 of the unit signal cycle 171 of multiplicity 2 by time axis, the time-aggregated signal of multiplicity 2 (shifted unit signal) 173 can be obtained. Here, the left-pointing arrows indicate time shift amounts with respect to the original unit signal at each frequency (the time shift amount corresponds to the above calculated nd (fc) assuming that each frequency is fc).
　次に、推定部７は、時間集約信号の強度分布Ｓ’（ｔ，ｆ）から、フレームｔ毎に特定の周波数帯域ｂに含まれる強度を求め、強度時系列Ｂ（ｔ）とする（ステップＳＴ４３）。さらに、推定部７は、強度時系列Ｂ（ｔ）においてピークを検出する（ステップＳＴ４４）。ピーク検出は強度時系列Ｂ（ｔ）の極大値を検出することで実行する。ここで、強度時系列Ｂ（ｔ）から、ピークを検出する際、受音信号に含まれる単位信号成分以外の成分をピークとして誤検出することがあるため、検出されたピーク間の間隔を計り、ピーク間の間隔が単位信号周期から外れる場合、そのピークを除去する（ステップＳＴ４５）。なお、このステップＳＴ４５の処理は必要に応じて行い、省略することも可能である。
　最後に、推定部７は、検出したピークを結ぶピーク値包絡線を抽出し（ステップＳＴ４６）、時間シフトによる時刻の遅れを補正して（ステップＳＴ４７）、推定した伝播特性として、出力する（ステップＳＴ４８）。 Next, the estimation unit 7 obtains the intensity included in the specific frequency band b for each frame t from the intensity distribution S ′ (t, f) of the time-aggregated signal and sets it as an intensity time series B (t) (step ST43). Furthermore, the estimation unit 7 detects a peak in the intensity time series B (t) (step ST44). Peak detection is performed by detecting the maximum value of the intensity time series B (t). Here, when detecting a peak from the intensity time series B (t), a component other than the unit signal component included in the sound reception signal may be erroneously detected as a peak, so the interval between the detected peaks is measured. , If the interval between peaks deviates from the unit signal cycle, the peaks are removed (step ST45). The process of step ST45 may be performed as necessary and may be omitted.
Finally, the estimation unit 7 extracts the peak value envelope connecting the detected peaks (step ST46), corrects the time delay due to the time shift (step ST47), and outputs it as the estimated propagation characteristic (step ST47) ST 48).
　実施の形態２で説明した多重化していない時間集約信号（図１３）と、実施の形態３の多重化した時間集約信号（図１７）を比較すると、多重化により、時間集約信号１７３を求める際の時間シフトの最大量が、多重化によりｍ分の１となり、全体の遅延が改善されている。また、伝播特性のサンプリング間隔は、単位信号周期１７１のｍ分の１となり、サンプリング間隔も改善されている。 Comparing the non-multiplexed time-aggregated signal described in the second embodiment (FIG. 13) with the multiplexed time-aggregated signal of the third embodiment (FIG. 17), the time-aggregate signal 173 is obtained by multiplexing. The maximum amount of time shift is reduced to 1 / m by multiplexing, and the overall delay is improved. Further, the sampling interval of the propagation characteristic is 1 / m of the unit signal period 171, and the sampling interval is also improved.
　図１８は、多重度８の単位信号配列による、伝播特性の計測例を示す説明図である。図１８Ａは時間周波数強度分布、図１８Ｂは時間シフト結果、図１８Ｃはピーク検出結果、図１８Ｄはピーク値包絡線（伝播特性推定結果）を示している。これらの図では、０～２２０５０Ｈｚの周波数帯域の内、０～８０００Ｈｚの周波数帯域のみを示している。従って、多重度が２程度であるように見えているが、実際は０～２２０５０Ｈｚの帯域において、多重度８の単位信号配列を用いている。また、これらの図において横軸は時間（秒）を示し、図１８Ａと図１８Ｂの縦軸は周波数（Ｈｚ）を示し、図１８Ｃと図１８Ｄの縦軸は単位信号成分の強度を示している。 FIG. 18 is an explanatory view showing an example of measurement of propagation characteristics by a unit signal arrangement of multiplicity 8; 18A shows a time frequency intensity distribution, FIG. 18B shows a time shift result, FIG. 18C shows a peak detection result, and FIG. 18D shows a peak value envelope (propagation characteristic estimation result). In these figures, only the frequency band of 0 to 8000 Hz is shown among the frequency band of 0 to 22050 Hz. Therefore, although the multiplicity appears to be about 2, in practice a unit signal array of multiplicity 8 is used in the band of 0 to 22050 Hz. In these figures, the horizontal axis represents time (seconds), the vertical axes in FIGS. 18A and 18B represent frequencies (Hz), and the vertical axes in FIGS. 18C and 18D represent intensities of unit signal components. .
　図１８Ａに示すように、６秒付近に外乱成分（縦縞）１８１が混入している。これが、図１８Ｂの時間シフト後の外乱１８２に示すように、周波数を矢印１８３に示すように分割した時間シフトによって分散されている。これにより、図１８Ｃに示すように、ピーク検出結果に、外乱によるピークが検出されず、その結果、良好なピーク値包絡線が推定されている（図１８Ｄ参照）。また、多重度８としたため、単位信号周期の１／８の時間的に稠密な推定結果となっている。 As shown in FIG. 18A, a disturbance component (vertical stripes) 181 is mixed in the vicinity of 6 seconds. This is dispersed by the time shift dividing the frequency as shown by arrow 183, as shown by disturbance 182 after the time shift in FIG. 18B. Thereby, as shown in FIG. 18C, the peak due to the disturbance is not detected in the peak detection result, and as a result, a good peak value envelope is estimated (see FIG. 18D). Further, since the multiplicity is 8, it is a densely estimated result of 1/8 of the unit signal period in time.
　以上説明したように、実施の形態３の音響計測システムによれば、測定部は、試験信号として、それぞれタイミングをずらした複数の単位信号を時間軸上で多重した単位信号配列を用いるようにしたので、伝播特性の時刻に対する変化が速い場合でも伝播特性を良好に計測することができる。 As described above, according to the sound measurement system of the third embodiment, the measurement unit uses, as the test signal, a unit signal array in which a plurality of unit signals shifted in timing are multiplexed on the time axis. Therefore, even when the change of the propagation characteristic with respect to time is fast, the propagation characteristic can be measured well.
　また、実施の形態３の音響計測システムによれば、推定部は、多重した単位信号配列の多重度に応じて周波数を分割し、分割毎に、単位信号の強度が同一時刻になるよう周波数毎に時間軸をシフトさせた上で伝播特性を求めるようにしたので、伝播特性の時刻に対する変化が速い場合でも精度良く伝播特性を推定することができる。 Further, according to the sound measurement system of the third embodiment, the estimation unit divides the frequency in accordance with the multiplicity of the multiplexed unit signal array, and for each frequency, the frequency of the unit signal is equal at each division. Since the propagation characteristic is determined after shifting the time axis, the propagation characteristic can be accurately estimated even when the change of the propagation characteristic with respect to time is fast.
　なお、上記各実施の形態では、受音体としての音センサ２を乗車カゴ１の一箇所に設けたが、複数箇所に設置し、複数の音センサ２からの試験音を取得するようにしてもよい。
　また、上記各実施の形態では、発音体５を移動しない側（固定側）、音センサ２を移動する側（移動側）に設ける例を説明したが、これに限定されるものではなく、発音体５を移動側、音センサ２を固定側に設置する装置でも同様に適用可能である。例えば、交差点における車両事故音の監視装置では、交差点の信号柱に音センサ２を設置し、事故車両音を監視する構成であるが、これに対しても同様に適用可能である。
　さらに、上記各実施の形態では、エレベータシステムへの適用例を説明したが、これ以外にも、プラントにおける移動体の音把握、移動ロボットによる音把握、車両やエスカレータ等の移動体を含む機器の音把握の構成に対しても同様に適用可能である。 In each of the above embodiments, the sound sensor 2 as the sound receiving body is provided at one place of the riding basket 1, but it may be installed at a plurality of places to obtain test sounds from the plurality of sound sensors 2. It is also good.
In each of the above-described embodiments, an example is described in which the sounding body 5 is not moved (fixed side) and the sound sensor 2 is moved (moving side), but the present invention is not limited to this. The same applies to an apparatus in which the body 5 is moved and the sound sensor 2 is fixed. For example, in the monitoring apparatus for vehicle accident sound at an intersection, the sound sensor 2 is installed on a signal pole of the intersection to monitor the sound of the accident vehicle, but the present invention is applicable to this as well.
Furthermore, although the application example to the elevator system has been described in each of the above-described embodiments, in addition to this, the equipment including the moving object such as the vehicle and escalator etc. The same can be applied to the configuration of sound grasping.
　なお、本願発明はその発明の範囲内において、各実施の形態の自由な組合せ、あるいは各実施の形態の任意の構成要素の変形、もしくは実施の形態の任意の構成要素の省略が可能である。 Within the scope of the present invention, the present invention allows free combination of each embodiment, or modification of any component of each embodiment, or omission of any component of the embodiment.
　以上のように、この発明に係る音響計測システム及びパラメータ生成装置は、時刻と共に伝播特性が変化する場合に、その伝播特性を求める構成に関するものであり、例えばエレベータの異常音診断装置に用いるのに適している。 As described above, the acoustic measurement system and the parameter generation device according to the present invention relate to a configuration for obtaining the propagation characteristic when the propagation characteristic changes with time, for example, for use in an abnormal sound diagnosis apparatus for an elevator Is suitable.
　１　乗車カゴ、２　音センサ（受音体）、３　コンピュータ、４　計測対象、５　発音体、６　測定部、７　推定部、８　模擬音合成部、９　音源データベース、１０　シミュレーション部、１１　パラメータ記憶部、２１　音響計測システム、２２　パラメータ生成装置、２１ａ　伝播特性。 DESCRIPTION OF SYMBOLS 1 ride basket, 2 sound sensor (sound receiving body), 3 computer, 4 measurement object, 5 sounding body, 6 measurement unit, 7 estimation unit, 8 simulated sound synthesis unit, 9 sound source database, 10 simulation unit, 11 parameter storage unit , 21 acoustic measurement system, 22 parameter generator, 21a propagation characteristics.

Claims (7)

  1. 　計測対象に設けた発音体と、
    　受音点に設けた受音体と、
    　各時刻で一つの周波数成分を有し、当該周波数成分の中心周波数が時間と共に変化する単位信号を時間軸上に配列した試験信号を前記発音体から前記受音体までの間を伝播させ、前記受音体で得られる試験信号を取得する測定部と、
    　前記試験信号に含まれる単位信号の時間と強度の関係に基づいて、前記発音体から前記受音体までの間の音の伝播特性を推定する推定部とを備えたことを特徴とする音響計測システム。     The sounding body provided for the measurement object,
    A sound receiving body provided at the sound receiving point,
    A test signal in which unit signals having one frequency component at each time point and the center frequency of the frequency component changing with time are arranged on a time axis is propagated from the sound producing body to the sound receiving body; A measurement unit for acquiring a test signal obtained by the sound receiving body;
    Acoustic measurement characterized in that it comprises an estimation unit for estimating the propagation characteristic of sound from the sounding body to the sound receiving body based on the relationship between time and intensity of unit signals included in the test signal. system.
  2. 　前記推定部は、前記単位信号を、当該単位信号の強度が同一時刻になるよう、周波数毎に時間軸をシフトさせた上で前記伝播特性を求めることを特徴とする請求項１記載の音響計測システム。 The acoustic measurement according to claim 1, wherein the estimation unit determines the propagation characteristic after shifting the time axis for each frequency so that the intensity of the unit signal becomes the same time. system.
  3. 　前記測定部は、前記試験信号として、それぞれタイミングをずらした複数の単位信号を時間軸上で多重した単位信号配列を用いることを特徴とする請求項１記載の音響計測システム。 The sound measurement system according to claim 1, wherein the measurement unit uses, as the test signal, a unit signal array in which a plurality of unit signals shifted in timing are multiplexed on a time axis.
  4. 　前記推定部は、前記多重した単位信号配列の多重度に応じて周波数を分割し、分割毎に、前記単位信号の強度が同一時刻になるよう周波数毎に時間軸をシフトさせた上で前記伝播特性を求めることを特徴とする請求項３記載の音響計測システム。 The estimation unit divides the frequency according to the multiplicity of the multiplexed unit signal array, and shifts the time axis for each frequency so that the intensity of the unit signal becomes the same time for each division, and then the propagation is performed. The acoustic measurement system according to claim 3, wherein the characteristic is determined.
  5. 　前記単位信号を、時間引き延ばしパルス（ＴＳＰ：Ｔｉｍｅ　Ｓｔｒｅｔｃｈｅｄ　Ｐｕｌｓｅ）信号としたことを特徴とする請求項１記載の音響計測システム。 The sound measurement system according to claim 1, wherein the unit signal is a time stretched pulse (TSP) signal.
  6. 　請求項１～５のうちのいずれかに記載の音響計測システムを用い、
    　前記推定された伝播特性を用いて、前記計測対象が正常状態か異常状態かを判定するためのパラメータを生成することを特徴とするパラメータ生成装置。     The acoustic measurement system according to any one of claims 1 to 5 is used,
    A parameter generating apparatus characterized by generating a parameter for determining whether the measurement target is in a normal state or an abnormal state using the estimated propagation characteristic.
  7. 　前記推定部で推定された伝播特性を用いて合成模擬音を生成する模擬音合成部と、
    　前記合成模擬音を用いて前記パラメータを決定するシミュレーション部とを備えたことを特徴とする請求項６記載のパラメータ生成装置。     A simulated sound synthesis unit that generates a synthetic simulated sound using the propagation characteristics estimated by the estimation unit;
    The parameter generation device according to claim 6, further comprising: a simulation unit that determines the parameter using the synthetic simulated sound.
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