CN106782490B - Noise processing method and device - Google Patents
Noise processing method and device Download PDFInfo
- Publication number
- CN106782490B CN106782490B CN201710058058.5A CN201710058058A CN106782490B CN 106782490 B CN106782490 B CN 106782490B CN 201710058058 A CN201710058058 A CN 201710058058A CN 106782490 B CN106782490 B CN 106782490B
- Authority
- CN
- China
- Prior art keywords
- signal
- noise
- sinusoidal
- frequency
- signals
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
-
- 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
- G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
- G10K2210/30—Means
- G10K2210/301—Computational
- G10K2210/3028—Filtering, e.g. Kalman filters or special analogue or digital filters
-
- 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
- G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
- G10K2210/30—Means
- G10K2210/301—Computational
- G10K2210/3045—Multiple acoustic inputs, single acoustic output
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Soundproofing, Sound Blocking, And Sound Damping (AREA)
Abstract
The invention relates to a noise processing method, which comprises the following steps: acquiring a plurality of sinusoidal reference signals, wherein the sinusoidal reference signals are generated by a sinusoidal signal generator and correspond to different frequencies; aiming at the sinusoidal reference signals, processing by adopting a plurality of filter-X least mean square algorithm filters in a parallel structure to obtain a plurality of reference noise signals; deriving a secondary noise signal based on the plurality of reference noise signals; and eliminating the external noise signal by using the secondary noise signal, wherein the amplitude of the secondary noise signal is the same as that of the external noise signal, and the phase of the secondary noise signal is opposite to that of the external noise signal. When the frequency of the reference signal is deviated from the actual frequency of the noise (frequency offset), the influence of the frequency offset on the system control effect can be reduced, so that the system control effect is more suitable for practical application, and the robustness of the system application is increased.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a noise processing method and apparatus.
Background
Noise pollution is widely present in industrial production and daily life, and periodic noise such as noise emitted by rotating or reciprocating equipment such as engines, compressors, fans and propellers is also very common. It is called narrowband noise because its noise energy is concentrated in a specific frequency band. With the progress of electronic technology, the active noise control technology is gradually developed, and compared with the traditional passive noise control technology, the active noise control technology has the advantages of low cost, high flexibility and the like.
At present, a DSP (Digital signal processing) processor is mainly used for realizing the active noise control system engineering, and has the advantages of relatively mature related driving and relatively low chip cost. However, as the frequency of the narrow-band noise increases, the required audio sampling rate also needs to be increased, so the amount of calculation increases accordingly, the calculation speed of the DSP becomes a bottleneck, and therefore, the current active noise control system is applied to a low-frequency noise scene. The active noise control has poor noise reduction effect on high-frequency noise, and under the condition of coping with multiple frequencies and multiple channels, the calculated amount is multiplied, so that the noise reduction efficiency is influenced.
The general active noise control algorithm is a Filtered-X LMS (Least Mean Square) algorithm, which takes a statistical LMS (Least Mean Square) theory as an analysis basis, and utilizes a filter equivalent to a secondary channel to preprocess a reference signal and inject a processing result into the LMS algorithm. When the frequency of the reference signal is deviated from the actual frequency of the noise (frequency offset), the performance of the algorithm is greatly reduced, the noise reduction effect is influenced, and the flexibility of the system is poor.
It is desirable to provide a noise processing method and apparatus to at least partially address the above-mentioned problems.
Disclosure of Invention
In view of the above problems, the present invention provides a noise processing method and apparatus, which do not affect the noise reduction effect when the frequency of the reference signal deviates from the actual frequency of the noise (frequency offset), and increase the flexibility of system application.
According to an aspect of the present invention, there is provided a noise processing method, the method including: acquiring a plurality of sinusoidal reference signals, wherein the sinusoidal reference signals are generated by a sinusoidal signal generator and correspond to different frequencies; aiming at the sinusoidal reference signals, processing by adopting a plurality of filter-X least mean square algorithm filters in a parallel structure to obtain a plurality of reference noise signals; deriving a secondary noise signal based on the plurality of reference noise signals; and eliminating the external noise signal by using the secondary noise signal, wherein the amplitude of the secondary noise signal is the same as that of the external noise signal, and the phase of the secondary noise signal is opposite to that of the external noise signal.
According to another aspect of the present invention, there is provided a noise processing apparatus, the apparatus including: a sinusoidal reference signal acquisition module configured to acquire a plurality of sinusoidal reference signals, wherein the sinusoidal reference signals are generated by a sinusoidal signal generator, and the plurality of sinusoidal reference signals correspond to different frequencies; a sinusoidal reference signal processing module configured to process the plurality of sinusoidal reference signals by using a plurality of filter-X least mean square algorithm filters in a parallel structure to obtain a plurality of reference noise signals; a secondary noise signal acquisition module configured to derive a secondary noise signal based on the plurality of reference noise signals; an ambient noise signal cancellation module configured to cancel an ambient noise signal using the secondary noise signal, the secondary noise signal having an amplitude that is the same as an amplitude of the ambient noise signal, and the secondary noise signal having a phase that is opposite to the phase of the ambient noise signal.
According to the noise processing method and device provided by the embodiment of the invention, the corresponding filtering-X least mean square algorithm filter is adopted for processing each reference signal, when the frequency of the reference signal has deviation (frequency offset) with the actual frequency of the noise, the influence of the frequency offset on the control effect of the system is reduced, the robustness of the system is enhanced, the method and device are more suitable for actual application, and the flexibility of the application of the system is increased.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail embodiments of the present invention with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings, like reference numbers generally represent like parts or steps.
FIG. 1 shows a schematic diagram of the principle structure of a narrow-band feedforward FxLMS algorithm;
FIG. 2 is a flow chart illustrating a noise processing method according to an embodiment of the present invention;
FIG. 3 shows a schematic structural diagram of a parallel structure FxLMS algorithm of an embodiment of the present invention;
FIG. 4 shows a multiplier resource multiplexing timing diagram of an embodiment of the invention;
fig. 5 shows a schematic block diagram of a noise processing apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the invention described herein without inventive step, shall fall within the scope of protection of the invention.
There are two implementations of feedforward Active Noise (ANC) systems, the main difference being the source of the reference signal. Although algorithms that use acoustic sensors to collect primary noise as a reference signal can actively control for both wideband and narrowband noise, acoustic feedback problems can severely impact the stability of the algorithms. In the embodiment of the invention, the narrow-band noise signal is actively controlled, the main frequency of the noise signal can be calculated by utilizing non-acoustic parameters collected by non-acoustic sensors such as an accelerometer, a tachometer and the like, and then a reference signal is generated in the system.
The structure of the narrowband feedforward filtering-X least mean square (FxLMS) algorithm is shown in fig. 1. The algorithm mainly comprises three parts of reference signal generation, off-line identification of a secondary sound channel and least mean square algorithm (LMS) adaptive filtering. As shown in FIG. 1, where W (z) represents a FIR (Finite Impulse Response) filter, P (z) represents the primary acoustic channel, and S (z) represents the generationThe table is converted from a secondary noise digital to a secondary acoustic channel between the microphone acquisition and the audio data,
is an M order FIR estimate of the secondary channel s (z). The secondary channel can cause system instability, and in order to compensate the influence of the secondary channel, the FxLMS algorithm needs to obtain M-order FIR estimation of the secondary channel S (z)
Assuming that the secondary acoustic channel is a time-invariant system and that the primary noise is not present in the identification process, an off-line identification method may be employed. The dotted line in FIG. 1 is the off-line identification module used in the system. The transfer function of the secondary acoustic channel obtained after stabilization is formula (1-1)
The function of the FxLMS algorithm is to adaptively change the coefficients of the FIR filter W (z), and assuming that the length of the filter is L and u is the step factor of the algorithm, the FxLMS algorithm can be expressed as formula (1-2) (1-3) (1-4) (1-5)
e(n)＝d(n)+y(n) (1-5)
In practical applications, the narrowband noise does not only contain one frequency component, but often also includes higher harmonics of its fundamental frequency or other frequency components with higher energy. In order to solve this problem, the sinusoidal signal generator may include a plurality of corresponding frequency components, which has the advantages of simple algorithm structure and low resource consumption. However, when the frequency components in the noise have large differences, the same step size factor cannot make all the frequencies reach the optimal convergence state, and oscillation or non-convergence occurs. When the difference between the frequency components is small, the order of the adaptive filter needs to be long enough to satisfy the high resolution of the adjacent frequencies, which results in the problems of slow convergence speed, large quantization error, and the like.
Therefore, the embodiment of the present invention provides a noise processing method for solving the above problems, which can process each frequency component separately, that is, adopt the FxLMS algorithm with a parallel structure.
A noise processing method provided by the present invention will be described in detail below with reference to the accompanying drawings, so that those skilled in the art can clearly and accurately understand the technical solution of the present invention.
Fig. 2 is a flow chart of a noise processing method according to an embodiment of the present invention.
As shown in fig. 2, a noise processing method may include the steps of:
In this step, the main frequency of the noise signal can be calculated by using non-acoustic parameters collected by non-acoustic sensors such as an accelerometer and a tachometer, and then a sinusoidal reference signal is generated inside the system. In some examples, the sinusoidal reference signal may be obtained by detecting an acceleration or a rotational speed of a noise source (e.g., an automobile engine), determining a reference frequency (dominant frequency) of a primary reference signal (noise signal generated by the noise source) from the acceleration or the rotational speed, and iteratively calculating a taylor series expansion of the sinusoidal signal from the reference frequency.
Since the operation speed of an FPGA (Field-Programmable Gate Array) is fast, in order to improve the overall operation speed, in the embodiment of the present invention, a sine signal generator is implemented in the FPGA. Since the FPGA can only generate the sinusoidal reference signal in a hardware form, the calculation method of the sinusoidal signal is changed in the embodiment of the present invention. Illustratively, the numerical value of the sinusoidal signal is calculated in an iterative manner by using a Taylor series expansion of the sinusoidal signal, and the iterative calculation is performed by using an iterative formula when the fixed point decimal place is N, wherein the iterative formula is shown as (1-6), (1-7) and (1-8).
x(n)＝C×x(n-1)-x(n-2),n≥3 (1-6)
x(0)＝0，x(1)＝round(0.01×2N) (1-7)
C＝round(2×cos(2πf/fs)×2N) (1-8)
Wherein f issFor the signal sampling rate, f is the sinusoidal signal frequency and round is a rounding function.
Further, the plurality of sinusoidal reference signals include a first reference signal, a second reference signal and a third reference signal, wherein the frequency of the second reference signal and the frequency of the third reference signal are determined according to the product value of the reference frequency and the preset offset. In the practical application of the narrow-band ANC system, if the frequency of the reference signal is different from the frequency of the primary noise, i.e. a so-called frequency offset phenomenon occurs, the control capability of the system for the frequency noise is sharply reduced. In order to determine the influence of the frequency deviation degree on the noise control capability of the system, simulation analysis is performed on the conditions of different frequencies and different degrees of frequency deviation, as shown in table 1:
TABLE 1 Effect of frequency offset on different frequency noise control
Tab.1 The effect offequency deviation of different frequency noiseeontrol
As can be seen from table 1, when the control capability of the frequency noise of a certain frequency component reaches more than 10dB, it is acceptable that the noise frequency is within 1%. Therefore, in order to increase the robustness of the system to the frequency offset, assuming that the frequency of the first reference signal is f in the embodiment of the present invention, the frequencies of the second reference signal and the third reference signal may be offset by 1% above and below f, for example, the frequency of the second reference signal may be 1.01f, and the frequency of the third reference signal is 0.99 f.
And step 220, aiming at the sinusoidal reference signals, processing by adopting a plurality of filtering-X least mean square algorithm filters in a parallel structure to obtain a plurality of reference noise signals.
In this step, for the sinusoidal reference signals of different frequency components, a filter-X least mean square algorithm filter is respectively set for processing, so that a plurality of sinusoidal reference signals are processed in parallel by a plurality of filter-X least mean square algorithm filters. When the frequency of the reference signal is deviated from the frequency of the noise signal, a good noise reduction effect can be achieved, and therefore the adaptability of the algorithm (or ANC system) is improved.
Illustratively, this step may be implemented as: and aiming at any one sinusoidal reference signal, performing multiplication operation through a filtering-X least mean square algorithm filter corresponding to the sinusoidal reference signal to obtain a reference noise signal, and outputting a plurality of reference noise signals by a plurality of filtering-X least mean square algorithm filters.
Fig. 3 shows a schematic structural diagram of the parallel-structured FxLMS algorithm according to an embodiment of the present invention. For a plurality of sinusoidal reference signals, the embodiments of the present invention may adopt a parallel-structured FxLMS filter as shown in fig. 3 for processing. As shown in fig. 3, for any sinusoidal reference signal, corresponding to an FxLMS filter, for example, the FxLMS filter performs multiplication operation thereon, for example, a sinusoidal reference signal with a frequency of 0.99f is processed by the corresponding FxLMS filter to output a reference noise signal y1,iThe sinusoidal reference signal with frequency f is processed by the corresponding FxLMS filter and then outputs the reference noise signal y2,iThe sinusoidal reference signal with frequency of 1.01f is processed by the corresponding FxLMS filter and then outputs the reference noise signal y3,i。
By the embodiment of respectively setting the filter-X least mean square algorithm filter for processing the sinusoidal reference signals with different frequency components, a good noise reduction effect can be achieved when the frequency of the reference signal is deviated from the frequency of the noise signal, so that the adaptability of the algorithm (or an ANC system) is improved.
However, since the filter-X least mean square algorithm filters have more multiply-add operations, increasing the number of filter-X least mean square algorithm filters will also increase the computational burden of the system. According to an embodiment of the present invention, the above problem can be solved in a time multiplexing manner. For example, the multiplication operations of the multiple filter-X least mean square algorithm filters may be calculated separately at different time ranges based on time multiplexing (or multiplier multiplexing). Taking the example that the FPGA realizes the multiplication operation of a plurality of filter-X least mean square algorithm filters, the working clock of the FPGA is 50MHz, and the sampling clock of the noise signal is 32KHz, so as to meet the condition that the system clock frequency is far greater than the data clock frequency, thereby multiplexing the multiplier by adopting a method of converting the multiplication operation which is carried out in parallel at the same moment into serial calculation, namely time multiplexing.
FIG. 4 shows a multiplier resource multiplexing timing diagram according to an embodiment of the invention. Assuming that N parallel filters with order M are provided, when the rising edge of the data clock comes, N × M multiplication results need to be calculated, that is, N × M multipliers are needed. On the other hand, if the multiplexing technique is adopted, the multiplication operations of each filter stage are calculated one by one, and as long as the time interval between two multiplication operations is longer than the time of the output result of the multiplier, the output result calculated by the N multipliers in series is correct, for example, the operation time interval T1 between multiplication (1) and multiplication (2) in fig. 4 is longer than the time T2 of the output result of multiplication (1). Therefore, the number of the multipliers consumed by the parallel filter is reduced to M, and the multiplier resources are greatly saved. As shown in fig. 4, as the number of multiplexing times increases, the difficulty of designing the scheduling module also increases accordingly. Because the system carries out multiplier multiplexing on algorithm modules of the same type, the scheduling module can be realized by adopting a simple counter, namely, counting the system clock when the data is valid (a node for starting multiplication), carrying out multiplication of each algorithm module at different counting values, and simultaneously outputting each multiplication result at different counting values.
Further exemplifying, by testing, for example, nine parallel FxLMS filters are used to implement the present invention, and if resource multiplexing is not used, the algorithm requires nine 128-order FIR filters and nine 64-order LMS adaptive filters in addition to secondary channel identification, for a total of 2121 multipliers. Due to the adoption of the multiplexing technology, a system algorithm comprises a 128-order secondary sound channel identification module, a nine-input nine-output 128-order FIR filter and a nine-input nine-output 64-order LMS adaptive filter, only 713 multipliers are needed, and multiplier resources are saved by nearly 2 times compared with those before multiplexing. Taking the EP3SE260F1152C2 chip in Stratix III series of Altera as an example, the chip has only 768 multipliers, if the resource multiplexing technique is not used, the chip cannot work at all, if the chip is replaced by another chip, the cost will also increase, and after the resource multiplexing (time multiplexing) technique of the multiplier of the present invention is used, the chip can complete the work. Thereby greatly reducing the system cost.
A secondary noise signal is derived based on the plurality of reference noise signals, step 230.
In this step, the plurality of reference noise signals may be added to obtain the secondary noise signal.
And 240, eliminating the external noise signal by using a secondary noise signal, wherein the amplitude of the secondary noise signal is the same as that of the external noise signal, and the phase of the secondary noise signal is opposite to that of the external noise signal.
In this step, the secondary noise signal and the external noise signal may be added to eliminate the external noise signal, so as to achieve the purpose of noise reduction.
According to an embodiment of the present invention, the filter-X least mean square algorithm filter may include an FIR filter and an LMS filter, and the noise processing method may further include the steps of:
after the secondary noise signal is used for eliminating the external noise signal, an error signal is obtained based on the secondary noise signal and the external noise signal, and the error signal is fed back to the corresponding LMS filter according to the frequency of the error signal, so that the LMS filter adjusts the step factor u of the FIR filter according to the following formula:
by the embodiment, the step factor of the FIR filter can be adjusted according to the actual situation so as to adapt to the situation that the multi-frequency noise signal can reach the optimal convergence state through the processing of the embodiment of the invention, thereby achieving the better noise reduction effect.
In summary, by applying the noise processing method provided by the embodiment of the present invention, for each reference signal, a corresponding filter-X least mean square algorithm filter is used for processing, and when the frequency of the reference signal has a deviation (frequency offset) from the actual frequency of the noise, the influence of the frequency offset on the system control effect is reduced, the robustness of the system is enhanced, the noise processing method is more suitable for actual application, and the flexibility of the system application is increased.
In addition, the application of the resource multiplexing (time multiplexing or multiplier multiplexing) technology greatly reduces the consumption of the system to the multiplier resource and reduces the implementation cost.
The embodiment of the invention adopts the FPGA with stronger parallel computing capability as the core processor, can actively control the high-frequency noise, and can solve the problem of multiplied increase of the computing quantity under the conditions of multiple frequencies and multiple channels.
The system can be applied to the control field of various narrow-band noises by matching with different noise frequency analysis algorithms. For example, for active control of engine noise in an automobile, the main frequency of the engine noise of the automobile can be analyzed by acquiring the signal of the engine of the automobile in real time, and then the system provided by the invention is used for active control.
In another aspect of the present invention, a noise processing apparatus applying the above noise processing method is provided. Fig. 5 shows a schematic block diagram of a noise processing apparatus according to an embodiment of the present invention, and as shown in fig. 5, the noise processing apparatus may include a sinusoidal reference signal acquisition module 510, a sinusoidal reference signal processing module 520, a secondary noise signal acquisition module 530, and an external noise signal cancellation module 540. And the sinusoidal reference signal acquisition module 510, the sinusoidal reference signal processing module 520, the secondary noise signal acquisition module 530, and the ambient noise signal cancellation module 540 may be provided in a processor, such as an FPGA.
Wherein the sinusoidal reference signal acquisition module 510 is configured to acquire a plurality of sinusoidal reference signals, wherein the sinusoidal reference signals are generated by a sinusoidal signal generator, and the plurality of sinusoidal reference signals correspond to different frequencies.
The sinusoidal reference signal processing module 520 is configured to process the plurality of sinusoidal reference signals with a plurality of filter-X least mean square algorithm filters in a parallel configuration to obtain a plurality of reference noise signals.
The secondary noise signal acquisition module 530 is configured to derive a secondary noise signal based on the plurality of reference noise signals.
The ambient noise signal cancellation module 540 is configured to cancel the ambient noise signal with a secondary noise signal, the secondary noise signal having the same amplitude as the ambient noise signal and having a phase opposite to the phase of the ambient noise signal.
For the implementation principle and the implementation effect of each module in the noise processing apparatus, reference may be made to the detailed description of the embodiment of the noise processing method, and for brevity, detailed description is not provided in this embodiment.
By applying the noise processing device provided by the embodiment of the invention, the corresponding filtering-X least mean square algorithm filter is adopted for processing each reference signal, and when the frequency of the reference signal has deviation (frequency offset) from the actual frequency of the noise, the influence of the frequency offset on the control effect of the system is reduced, the robustness of the system is enhanced, the noise processing device is more suitable for actual application, and the flexibility of the application of the system is increased.
Although the illustrative embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the foregoing illustrative embodiments are merely exemplary and are not intended to limit the scope of the invention thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as set forth in the appended claims.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, a division of a unit is only one type of division of a logical function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted, or not executed.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present invention should not be construed to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some of the modules in a wire suspension point locating device according to embodiments of the present invention. The present invention may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiment of the present invention or the description thereof, and the protection scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A method of noise processing, the method comprising:
detecting non-acoustic parameters of a noise source;
determining a reference frequency of a primary reference signal according to the non-acoustic parameters of the noise source, wherein the primary reference signal is a noise signal generated by the noise source;
and the number of the first and second groups,
the sinusoidal signal generator is used for carrying out iterative calculation on a Taylor series expansion of sinusoidal signals based on the reference frequency of the primary reference signal to obtain sinusoidal reference signals;
acquiring a plurality of sinusoidal reference signals, wherein the sinusoidal reference signals correspond to different frequencies;
aiming at the sinusoidal reference signals, processing by adopting a plurality of filter-X least mean square algorithm filters in a parallel structure to obtain a plurality of reference noise signals;
deriving a secondary noise signal based on the plurality of reference noise signals;
eliminating an external noise signal by using the secondary noise signal, wherein the amplitude of the secondary noise signal is the same as that of the external noise signal, and the phase of the secondary noise signal is opposite to that of the external noise signal; wherein the content of the first and second substances,
the plurality of sinusoidal reference signals includes a first reference signal, a second reference signal, and a third reference signal,
wherein the frequency of the first reference signal is the reference frequency;
and the frequency of the second reference signal and the frequency of the third reference signal are determined according to a product value of the reference frequency and a preset offset.
2. The noise processing method of claim 1, wherein the sinusoidal signal generator is provided in an FPGA processor,
and iteratively calculating the Taylor series expansion of the sinusoidal signal by the following formula:
x(n)＝C×x(n-1)-x(n-2)，n≥3；
x(0)＝0，x(1)＝round(0.01×2N) (ii) a And
C＝round(2×cos(2πf/fs)×2N)，
wherein f issFor the signal sampling rate, f is the sinusoidal signal frequency and round is a rounding function.
3. The method of claim 1, wherein the processing with the plurality of filter-X least mean square algorithm filters in a parallel configuration for the plurality of sinusoidal reference signals comprises:
for any one of the sinusoidal reference signals, multiplying the sinusoidal reference signal by a corresponding filter-X least mean square algorithm filter to obtain the reference noise signal,
a plurality of said filter-X least mean square algorithm filters output a plurality of said reference noise signals.
4. The noise processing method according to claim 1 or 3, wherein the deriving a secondary noise signal based on the plurality of reference noise signals comprises:
and adding the plurality of reference noise signals to obtain a secondary noise signal.
5. The noise processing method of claim 3, further comprising:
and calculating multiplication operations of the plurality of filter-X least mean square algorithm filters in a serial mode respectively.
6. The method of claim 5, wherein the calculating multiplication operations of the plurality of filter-X least mean square algorithm filters in a serial manner comprises:
and respectively calculating multiplication operations of the plurality of filtering-X least mean square algorithm filters in different time ranges based on time multiplexing.
7. The noise processing method of claim 1, wherein the filter-X least mean square algorithm filter comprises a finite long single-bit impulse response filter and a least mean square algorithm filter,
the method further comprises the following steps:
after cancelling the ambient noise signal with the secondary noise signal, deriving an error signal based on the secondary noise signal and the ambient noise signal,
feeding back the error signal to a corresponding least mean square algorithm filter according to the frequency of the error signal, so that the least mean square algorithm filter adjusts the step size factor of the finite length unit impulse response filter according to the following formula:
8. a noise processing apparatus, characterized in that the apparatus comprises:
a sinusoidal reference signal acquisition module configured to acquire a plurality of sinusoidal reference signals, wherein the plurality of sinusoidal reference signals correspond to different frequencies;
a sinusoidal reference signal processing module configured to process the plurality of sinusoidal reference signals by using a plurality of filter-X least mean square algorithm filters in a parallel structure to obtain a plurality of reference noise signals;
a secondary noise signal acquisition module configured to derive a secondary noise signal based on the plurality of reference noise signals;
an ambient noise signal cancellation module configured to cancel an ambient noise signal using the secondary noise signal, the secondary noise signal having an amplitude that is the same as an amplitude of the ambient noise signal, the secondary noise signal having a phase that is opposite to the phase of the ambient noise signal;
the apparatus further comprises:
a non-acoustic sensor to detect a non-acoustic parameter of the noise source;
determining a reference frequency of a primary reference signal according to the non-acoustic parameters of the noise source, wherein the primary reference signal is a noise signal generated by the noise source;
and the number of the first and second groups,
the sinusoidal signal generator is used for carrying out iterative calculation on a Taylor series expansion of sinusoidal signals based on the reference frequency of the primary reference signal to obtain sinusoidal reference signals;
wherein the plurality of sinusoidal reference signals includes a first reference signal, a second reference signal, and a third reference signal,
wherein the frequency of the first reference signal is the reference frequency;
and the frequency of the second reference signal and the frequency of the third reference signal are determined according to a product value of the reference frequency and a preset offset.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710058058.5A CN106782490B (en) | 2017-01-23 | 2017-01-23 | Noise processing method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710058058.5A CN106782490B (en) | 2017-01-23 | 2017-01-23 | Noise processing method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106782490A CN106782490A (en) | 2017-05-31 |
| CN106782490B true CN106782490B (en) | 2020-04-28 |
Family
ID=58942073
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710058058.5A Active CN106782490B (en) | 2017-01-23 | 2017-01-23 | Noise processing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106782490B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109138100B (en) * | 2018-07-03 | 2023-10-27 | 中国船舶重工集团公司第七一九研究所 | Intelligent mute system of flush toilet |
| CN109346054B (en) * | 2018-10-23 | 2021-11-02 | 超越科技股份有限公司 | Active noise reduction method and device |
| CN109582176B (en) * | 2018-11-30 | 2021-12-24 | 北京集创北方科技股份有限公司 | Anti-noise method and device for touch screen |
| CN111369964B (en) * | 2018-12-26 | 2023-08-18 | 宁波方太厨具有限公司 | White noise sleep aiding method and device based on active noise reduction |
| CN111402853B (en) * | 2020-03-02 | 2022-11-29 | 吉林大学 | Wide-band and narrow-band hybrid active noise reduction algorithm suitable for interior of vehicle |
| CN111144036B (en) * | 2020-03-02 | 2023-03-28 | 上海索辰信息科技股份有限公司 | Multi-scale simulation analysis method for propeller noise |
| CN111220513A (en) * | 2020-03-23 | 2020-06-02 | 石家庄铁道大学 | Haze detection device and method based on digital phase locking and photoacoustic spectroscopy technology |
| CN111724761B (en) * | 2020-05-21 | 2023-01-17 | 中科上声（苏州）电子有限公司 | Vehicle-mounted active noise control device and method |
| CN111883097A (en) * | 2020-08-05 | 2020-11-03 | 西安艾科特声学科技有限公司 | Train cab active noise control system based on virtual sensing |
| CN111983698B (en) * | 2020-08-20 | 2023-05-12 | 吉林大学 | Component phase inversion-based drag transient electromagnetic data power frequency noise suppression method |
| CN112309362A (en) * | 2020-10-30 | 2021-02-02 | 上海联影医疗科技股份有限公司 | Active acoustic noise reduction method and device of MR system and computer equipment |
| CN113593515B (en) * | 2021-07-15 | 2023-11-24 | 江南大学 | Wide-narrow band hybrid active noise control system for coping with frequency offset |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976560A (en) * | 2010-09-29 | 2011-02-16 | 哈尔滨工业大学 | Method for improving performance of feedforward narrow-band active noise control system |
| CN105244016A (en) * | 2015-11-19 | 2016-01-13 | 清华大学深圳研究生院 | Active noise reduction system and method |
| CN105575382A (en) * | 2015-12-29 | 2016-05-11 | 哈尔滨工业大学 | Complete parallel narrow-band active noise control method with rapid and stable convergence |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2787502B1 (en) * | 2013-04-05 | 2021-03-10 | BlackBerry Limited | Active noise equalization |
-
2017
- 2017-01-23 CN CN201710058058.5A patent/CN106782490B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976560A (en) * | 2010-09-29 | 2011-02-16 | 哈尔滨工业大学 | Method for improving performance of feedforward narrow-band active noise control system |
| CN105244016A (en) * | 2015-11-19 | 2016-01-13 | 清华大学深圳研究生院 | Active noise reduction system and method |
| CN105575382A (en) * | 2015-12-29 | 2016-05-11 | 哈尔滨工业大学 | Complete parallel narrow-band active noise control method with rapid and stable convergence |
Non-Patent Citations (3)
| Title |
|---|
| 《一种多通道信号处理复用结构及其FPGA实现方法》;刘纯武等;《电子测量与仪器学报》;20080229;第22卷(第1期);第72-75页 * |
| 《乘法器复用技术在滑窗FIR滤波处理中的运用》;陈风波等;《微计算机信息》;20100927;第26卷(第8期);第19、140-141页 * |
| 《基于查找表和Taylor展开的正余弦函数的实现》;刘小明等;《现代电子技术》;20090728(第13期);第165-166、170页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106782490A (en) | 2017-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106782490B (en) | Noise processing method and device | |
| JP5640063B2 (en) | Adjustable active noise control | |
| CN204006502U (en) | Control device for noise reduction of air conditioner | |
| WO2017048480A1 (en) | Estimating secondary path phase in active noise control | |
| CN105318490A (en) | Control method and device for noise reduction of air conditioner | |
| Luo et al. | Efficient combination of feedforward and feedback structures for nonlinear narrowband active noise control | |
| US10083683B2 (en) | Reducing computer fan noise | |
| CN105913836B (en) | A kind of fixed point real-time noise-reducing method based on DSP | |
| Kuo et al. | Saturation effects in active noise control systems | |
| US20100111318A1 (en) | Active noise controller | |
| US10171907B1 (en) | Fan noise controlling system | |
| CN101577536A (en) | Method for realizing improved LMS algorithm | |
| CN106782594A (en) | One kind suppresses the unmatched method of frequency in arrowband active noise controlling | |
| KR101696597B1 (en) | Noise control method | |
| Wang et al. | New feedback active noise control system with improved performance | |
| CN114576568A (en) | Pipeline leakage detection method and device based on infrasonic waves | |
| Ku et al. | Controller design for active noise control of compressor by using the time window POCS technique | |
| CN102394591B (en) | Field programmable gate array-based (FPGA-based) vehicle vibration signal wave filtering device | |
| CN106205596A (en) | A kind of noise reduction method, silencing means and there is the audible device of this device | |
| JP2770286B2 (en) | Vibration noise control device | |
| JPH0827634B2 (en) | Electronic silencing system | |
| CN204084797U (en) | Noise reduction system of air conditioning unit | |
| JP3411611B2 (en) | Noise cancellation method | |
| Sun et al. | A narrowband active noise control system with frequency mismatch compensation | |
| Lee et al. | Analysis of frequency-domain active noise control algorithm with parallel structure |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |