CN108234756B - Call control method, device and computer readable storage medium - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
- H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
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- G—PHYSICS
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- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
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- G10L25/21—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being power information
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- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
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Abstract
The present disclosure relates to a call control method, device and computer-readable storage medium, the method comprising: when a call trigger signal is detected, acquiring an in-vehicle noise signal of a vehicle; analyzing the time domain and the frequency domain of the in-vehicle noise signal, and extracting a characteristic value of the in-vehicle noise signal; judging whether the vehicle is in a running state or not according to the characteristic value of the in-vehicle noise signal; and refusing to respond to the call trigger signal when the vehicle is in a driving state. Through the technical scheme disclosed by the invention, a driver can be forcibly prohibited from using the mobile terminal to communicate when the vehicle is in a running state, so that the safe running of the vehicle is ensured, and the accident rate is reduced.
Description
Technical Field
The present disclosure relates to the field of terminal technologies, and in particular, to a call control method and apparatus, and a computer-readable storage medium.
Background
Many drivers are used to make calls using mobile terminals such as mobile phones while driving, however, this tends to distract the driver and increase the response time, which is likely to cause traffic accidents.
Disclosure of Invention
In view of the above, the present disclosure provides a call control method, device and computer-readable storage medium for forcibly prohibiting a driver from using a mobile terminal to make a call when a vehicle is in a driving state.
In order to achieve the above object, according to a first aspect of an embodiment of the present disclosure, there is provided a call control method including:
when a call trigger signal is detected, acquiring an in-vehicle noise signal of a vehicle;
analyzing the time domain and the frequency domain of the in-vehicle noise signal, and extracting a characteristic value of the in-vehicle noise signal;
judging whether the vehicle is in a running state or not according to the characteristic value of the in-vehicle noise signal;
and refusing to respond to the call trigger signal when the vehicle is in a driving state.
Optionally, the performing time domain and frequency domain analysis on the in-vehicle noise signal to extract a characteristic value of the in-vehicle noise signal includes:
extracting continuous noise signal segments with amplitude values within a preset range from the in-vehicle noise signals;
carrying out Fourier transform on the continuous noise signal segment to obtain a frequency spectrum of the continuous noise signal segment;
and acquiring preset octave power spectral density of the continuous noise signal segment according to the frequency spectrum of the continuous noise signal segment, wherein the characteristic value comprises the frequency spectrum of the continuous noise signal segment and the preset octave power spectral density.
Optionally, the determining whether the vehicle is in a driving state according to the characteristic value of the in-vehicle noise signal includes:
and if the frequency spectrum of the continuous noise signal segment meets a preset condition and the preset octave power spectral density of the continuous noise signal segment is smaller than a preset power spectral density threshold value, determining that the vehicle is in a running state.
Optionally, the method further comprises:
acquiring in-vehicle noise signals of different vehicles in different driving states as training samples;
analyzing the time domain and the frequency domain of the training sample to obtain the frequency spectrum and the preset octave power spectral density of the training sample;
and setting the preset condition according to the frequency spectrum of the training sample and setting the preset power spectral density threshold according to the preset octave power spectral density of the training sample.
According to a second aspect of the embodiments of the present disclosure, there is provided a call control device including:
the noise signal acquisition module is used for acquiring an in-vehicle noise signal of the vehicle when the call trigger signal is detected;
the characteristic value extraction module is used for analyzing the time domain and the frequency domain of the in-vehicle noise signal and extracting the characteristic value of the in-vehicle noise signal;
the driving state judging module is used for judging whether the vehicle is in a driving state or not according to the characteristic value of the noise signal in the vehicle;
and the response rejection module is used for rejecting to respond to the call trigger signal when the vehicle is in a running state.
Optionally, the feature value extraction module includes:
the noise signal segment extraction submodule is used for extracting a continuous noise signal segment of which the amplitude is within a preset range from the in-vehicle noise signal;
the frequency spectrum acquisition submodule is used for carrying out Fourier transform on the continuous noise signal segment to obtain the frequency spectrum of the continuous noise signal segment;
and the power spectral density acquisition sub-module is used for acquiring the preset octave power spectral density of the continuous noise signal segment according to the frequency spectrum of the continuous noise signal segment, wherein the characteristic value comprises the frequency spectrum of the continuous noise signal segment and the preset octave power spectral density.
Optionally, the driving state determination module includes:
and the driving state determining submodule is used for determining that the vehicle is in a driving state when the frequency spectrum of the continuous noise signal section meets a preset condition and the preset octave power spectral density of the continuous noise signal section is smaller than a preset power spectral density threshold value.
Optionally, the apparatus further comprises:
the training sample acquisition module is used for acquiring in-vehicle noise signals of different vehicles in different driving states as training samples;
the training sample analysis module is used for carrying out time domain and frequency domain analysis on the training sample to obtain the frequency spectrum and the preset power spectral density of the training sample;
and the setting module is used for setting the preset condition according to the frequency spectrum of the training sample and setting the preset power spectral density threshold according to the preset octave power spectral density of the training sample.
According to a third aspect of the embodiments of the present disclosure, there is provided a call control device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the call control method provided by the embodiment of the disclosure.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of the first aspect of embodiments of the present disclosure.
According to the technical scheme, when the call trigger signal is detected, the in-vehicle noise signal of the vehicle is obtained, whether the vehicle is in a running state or not is judged according to the in-vehicle noise signal, and if the vehicle is in the running state, the call trigger signal is refused to be responded, so that a driver can be forcibly prohibited from using the mobile terminal to carry out call when the vehicle is in the running state, the safe running of the vehicle is ensured, and the accident rate is reduced.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
fig. 1 is a flow chart illustrating a call control method according to an exemplary embodiment of the present disclosure;
fig. 2 is a flow chart illustrating a call control method according to another exemplary embodiment of the present disclosure;
FIG. 3 is a time domain schematic diagram of an in-vehicle noise signal shown in accordance with an exemplary embodiment of the present disclosure;
FIG. 4 is a spectrogram illustrating an in-vehicle noise signal according to an exemplary embodiment of the present disclosure;
fig. 5 is a preset octave power spectral density plot of an in-vehicle noise signal, according to an exemplary embodiment of the present disclosure;
fig. 6 is a flowchart illustrating a method of setting a spectrum preset condition and a preset power spectral density according to an exemplary embodiment of the present disclosure;
fig. 7 is a block diagram illustrating a call control device according to an exemplary embodiment of the present disclosure;
fig. 8 is a block diagram illustrating a call control device according to another exemplary embodiment of the present disclosure;
fig. 9 is a block diagram illustrating an apparatus for a call control method according to an exemplary embodiment of the present disclosure.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart illustrating a call control method according to an exemplary embodiment of the present disclosure, which may be applied to a mobile terminal, where the mobile terminal may be an electronic device with a call function, such as a mobile phone, a tablet computer, a smart wearable device, and the like, as shown in fig. 1, the method includes the following steps:
in step S101, when a call trigger signal is detected, an in-vehicle noise signal of the vehicle is acquired.
In an embodiment of the present disclosure, when a call trigger signal is detected, an in-vehicle noise signal of a vehicle can be acquired by using the normally open low-power microphone. The call trigger signal may include an incoming call signal and a dialed call signal.
In step S102, time domain and frequency domain analysis is performed on the in-vehicle noise signal to obtain a characteristic value of the in-vehicle noise signal.
In step S103, it is determined whether the vehicle is in a running state or not based on the feature value of the in-vehicle noise signal.
In step S104, when the vehicle is in a traveling state, the response to the call trigger signal is rejected.
The in-vehicle noise signal includes a noise signal emitted from an engine, tires, and the like of the vehicle, and the in-vehicle noise signal of the vehicle in the parking state and various driving states is different in terms of time domain and frequency domain. Therefore, the time domain and the frequency domain analysis are carried out on the obtained in-vehicle noise signal, the characteristic value of the in-vehicle noise signal is extracted, whether the vehicle is in the driving state at present can be determined according to the characteristic value, if the vehicle is in the driving state, the mobile terminal refuses to respond to the call trigger signal, and therefore the driver is prohibited from making a call in the driving process.
In an embodiment of the present disclosure, the characteristic value of the in-vehicle noise signal may include a frequency spectrum of the in-vehicle noise signal and a preset octave power spectral density, and accordingly, as shown in fig. 2, the step S102 may include:
in step S121, a continuous noise signal segment having an amplitude within a preset range is extracted from the in-vehicle noise signal.
The obtained in-vehicle noise signal may be mixed with the talking sound of the in-vehicle person, and further may affect the detection result. Since the in-vehicle noise signal is generally a steady-state signal which shows a small fluctuation and a continuous noise component in the time domain, in order to exclude the mixed in-vehicle person talking, as shown in fig. 3, a plurality of segments of the in-vehicle noise signal may be collected and a continuous noise signal segment having an amplitude within a preset range may be extracted from the noise signal segment, and the continuous noise signal segment may be regarded as the steady-state noise emitted from the engine and the tires of the vehicle.
In step S122, fourier transform is performed on the continuous noise signal segment to obtain a frequency spectrum of the continuous noise signal segment.
Next, fourier transform is performed on the extracted continuous noise signal segment, and a spectrum of the continuous noise signal segment is obtained as a spectrum of the in-vehicle noise signal, as shown in fig. 4.
In step S123, a preset octave power spectral density of the continuous noise signal segment is obtained according to the frequency spectrum of the continuous noise signal segment.
The frequency of noise signals such as an engine, tires and the like of a vehicle is mainly concentrated before 1kHz, so that after the frequency spectrum of the continuous noise signal segment is obtained, the frequency spectrum in the frequency range of 20 Hz-1 kHz can be divided according to a preset octave and the power spectral density of each frequency band can be calculated, and the preset octave power spectral density of the noise signal segment in the vehicle can be obtained.
In the embodiment of the present disclosure, the preset octaves may be 1/3 octaves, 1/6 octaves, 1/12 octaves, 1/24 octaves, and the like. The larger the frequency multiplication range number is, the higher the accuracy of the detection result is, but the robustness is poorer. Therefore, considering the accuracy and robustness of the detection result, preferably, the preset octave can be 1/12 octaves, and accordingly, the obtained 1/12 octave power spectral density is as shown in fig. 5.
In addition, since the noise signals of the engine and the tires of the vehicle are generally distributed in a low frequency range, and the accuracy of the identification result is considered, the power spectral densities of the first 10 frequency bands can be selected as one characteristic value of the noise signals in the vehicle for subsequent analysis.
Accordingly, in step S103, after the frequency spectrum and the preset octave power spectral density of the in-vehicle noise signal are obtained, the in-vehicle noise signal may be analyzed, and if the frequency spectrum of the in-vehicle noise signal satisfies the preset condition and the preset octave power spectral density is smaller than the preset power spectral density threshold, it may be determined that the vehicle is in the driving state.
In an embodiment of the present disclosure, the preset condition of the frequency spectrum and the preset power spectral density threshold may be set by a result of performing big data machine learning on a large number of accumulated in-vehicle noise signals of different brands of vehicles in different driving states, specifically, as shown in fig. 6, including:
in step S601, in-vehicle noise signals of different vehicles in different driving states are acquired as training samples.
In step S602, time domain and frequency domain analysis is performed on the training sample to obtain a frequency spectrum and a preset octave power spectral density of the training sample.
It should be noted that, in the process of obtaining the frequency spectrum of the training sample and the preset octave power spectral density, reference may be made to the process of performing time domain and frequency domain analysis on the in-vehicle noise signal to obtain the frequency spectrum of the in-vehicle noise signal and the preset octave power density in the above embodiment, which is not described herein again.
In step S603, the condition of the spectrum is set according to the spectrum of the training sample and the power spectral density threshold is set according to the preset octave power spectral density of the training sample.
For example, the training samples may be classified according to the driving state of the vehicle, that is, one driving state corresponds to one class of training samples, and for each class of training samples, the frequency spectrum and the preset octave power spectral density of each training sample in the class are obtained. By analyzing the time domain and the frequency domain of the training sample, the frequency spectrum and the preset octave power spectral density of the training sample can be obtained, and the upper limit value and the lower limit value of the frequency spectrum amplitude and the upper limit value of the preset octave power spectral density of the training sample can be set.
The method is characterized in that machine learning and analysis are carried out on frequency spectrums of various training samples and upper limit values and lower limit values of the set frequency spectrum amplitude, when a vehicle is in a driving state, the frequency spectrum of a noise signal in the vehicle shows a linear descending trend from low frequency to high frequency, and the upper limit value and the lower limit value of the frequency spectrum amplitude can be obtained, and correspondingly, the preset condition of the frequency spectrum is that the frequency spectrum shows a linear descending trend from the low frequency to the high frequency, and the amplitude is located between the upper limit value TH1 and the lower limit value TH 2.
Similarly, the power spectral density threshold TH3 may be set by machine learning and analyzing preset power spectral densities for various types of training samples.
It should be noted that, when setting the upper limit value and the lower limit value of the frequency spectrum of each type of training sample, a range may be preset first, and if the amplitude of the frequency spectrum is within the range, the range is gradually narrowed until the amplitude of the frequency spectrum is just within the range; if the magnitude of the spectrum is outside the range, the range is gradually expanded until the magnitude of the spectrum is just within the range. The upper limit of the power spectral density of each type of training sample is also applicable here, and is not described here again.
According to the technical scheme, when the call trigger signal is detected, the in-vehicle noise signal of the vehicle is obtained, whether the vehicle is in a running state or not is judged according to the in-vehicle noise signal, and if the vehicle is in the running state, the call trigger signal is refused to be responded, so that a driver can be forcibly prohibited from using the mobile terminal to carry out call when the vehicle is in the running state, the safe running of the vehicle is ensured, and the accident rate is reduced.
Fig. 7 is a block diagram of a call control apparatus 700 according to an exemplary embodiment of the present disclosure, where the apparatus 700 may be applied to a mobile terminal, where the mobile terminal may be an electronic device with a call function, such as a mobile phone, a tablet computer, a smart wearable device, and the like, and as shown in fig. 7, the apparatus 700 includes a noise signal obtaining module 701, a feature value extracting module 702, a driving state determining module 703, and a response rejection module 704.
The noise signal acquisition module 701 is configured to acquire an in-vehicle noise signal of a vehicle when a call trigger signal is detected;
the eigenvalue extraction module 702 is configured to perform time domain and frequency domain analysis on the in-vehicle noise signal, and extract an eigenvalue of the in-vehicle noise signal;
the driving state determining module 703 is configured to determine whether the vehicle is in a driving state according to the feature value of the in-vehicle noise signal;
the response rejection module 704 is configured to reject to respond to the call trigger signal when the vehicle is in a driving state.
Optionally, as shown in fig. 8, the feature value extraction module 702 includes:
the noise signal segment extraction submodule 721 is configured to extract a continuous noise signal segment whose amplitude is within a preset range from the in-vehicle noise signal;
the frequency spectrum obtaining submodule 722 is configured to perform fourier transform on the continuous noise signal segment to obtain a frequency spectrum of the continuous noise signal segment;
the power spectral density obtaining sub-module 723 is configured to obtain a preset octave power spectral density of the continuous noise signal segment according to the frequency spectrum of the continuous noise signal segment, where the feature value includes the frequency spectrum of the continuous noise signal segment and the preset octave power spectral density.
Alternatively, as shown in fig. 8, the driving state determination module 703 includes:
the driving state determining submodule 731 is configured to determine that the vehicle is in a driving state when the frequency spectrum of the continuous noise signal segment meets a preset condition and the preset octave power spectral density of the continuous noise signal segment is smaller than a preset power spectral density threshold.
Optionally, as shown in fig. 8, the apparatus 700 further includes:
a training sample acquisition module 705, configured to acquire in-vehicle noise signals of different vehicles in different driving states as training samples;
a training sample analysis module 706, configured to perform time domain and frequency domain analysis on the training sample to obtain a frequency spectrum and a preset power spectral density of the training sample;
a setting module 707, configured to set the preset condition according to the frequency spectrum of the training sample and set the preset power spectral density threshold according to the preset octave power spectral density of the training sample.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the call control method provided by the present disclosure.
Fig. 9 is a block diagram illustrating an apparatus 900 for a call control method according to an exemplary embodiment of the present disclosure. For example, the apparatus 900 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a tablet device, a personal digital assistant, and the like.
Referring to fig. 9, apparatus 900 may include one or more of the following components: a processing component 902, a memory 904, a power component 906, a multimedia component 908, an audio component 910, an input/output (I/O) interface 912, a sensor component 914, and a communication component 916.
The processing component 902 generally controls overall operation of the device 900, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. Processing component 902 may include one or more processors 920 to execute instructions to perform all or a portion of the steps of the call control method described above. Further, processing component 902 can include one or more modules that facilitate interaction between processing component 902 and other components. For example, the processing component 902 can include a multimedia module to facilitate interaction between the multimedia component 908 and the processing component 902.
The memory 904 is configured to store various types of data to support operation at the apparatus 900. Examples of such data include instructions for any application or method operating on device 900. The memory 904 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 908 comprises a screen providing an output interface between the device 900 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 900 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 910 is configured to output and/or input audio signals. For example, audio component 910 includes a Microphone (MIC) configured to receive external audio signals when apparatus 900 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 904 or transmitted via the communication component 916. In some embodiments, audio component 910 also includes a speaker for outputting audio signals.
I/O interface 912 provides an interface between processing component 902 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor component 914 includes one or more sensors for providing status assessment of various aspects of the apparatus 900. For example, sensor assembly 914 may detect an open/closed state of device 900, the relative positioning of components, such as a display and keypad of device 900, the change in position of device 900 or a component of device 900, the presence or absence of user contact with device 900, the orientation or acceleration/deceleration of device 900, and the change in temperature of device 900. The sensor assembly 914 may include a proximity sensor configured to detect the presence of a nearby object in the absence of any physical contact. The sensor assembly 914 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 914 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 916 is configured to facilitate communications between the apparatus 900 and other devices in a wired or wireless manner. The apparatus 900 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 916 receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 916 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 900 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described call control method.
In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 904 comprising instructions, executable by the processor 920 of the apparatus 900 to perform the call control method described above is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (6)
1. A call control method is characterized by comprising the following steps:
when a call trigger signal is detected, acquiring an in-vehicle noise signal of a vehicle;
analyzing the time domain and the frequency domain of the in-vehicle noise signal, and extracting a characteristic value of the in-vehicle noise signal;
judging whether the vehicle is in a running state or not according to the characteristic value of the in-vehicle noise signal;
refusing to respond to the call trigger signal when the vehicle is in a driving state;
wherein, the time domain and frequency domain analysis of the in-vehicle noise signal to extract the characteristic value of the in-vehicle noise signal comprises:
extracting continuous noise signal segments with amplitude values within a preset range from the in-vehicle noise signals;
carrying out Fourier transform on the continuous noise signal segment to obtain a frequency spectrum of the continuous noise signal segment;
acquiring preset octave power spectral density of the continuous noise signal segment according to the frequency spectrum of the continuous noise signal segment, wherein the characteristic value comprises the frequency spectrum of the continuous noise signal segment and the preset octave power spectral density;
the judging whether the vehicle is in a running state according to the characteristic value of the in-vehicle noise signal comprises the following steps:
and if the frequency spectrum of the continuous noise signal segment meets a preset condition and the preset octave power spectrum density of the continuous noise signal segment is smaller than a preset power spectrum density threshold value, determining that the vehicle is in a running state, wherein the preset condition is that the frequency spectrum is in a linear descending trend from low frequency to high frequency.
2. The method of claim 1, further comprising:
acquiring in-vehicle noise signals of different vehicles in different driving states as training samples;
analyzing the time domain and the frequency domain of the training sample to obtain the frequency spectrum and the preset octave power spectral density of the training sample;
and setting the preset condition according to the frequency spectrum of the training sample and setting the preset power spectral density threshold according to the preset octave power spectral density of the training sample.
3. A call control device, comprising:
the noise signal acquisition module is used for acquiring an in-vehicle noise signal of the vehicle when the call trigger signal is detected;
the characteristic value extraction module is used for analyzing the time domain and the frequency domain of the in-vehicle noise signal and extracting the characteristic value of the in-vehicle noise signal;
the driving state judging module is used for judging whether the vehicle is in a driving state or not according to the characteristic value of the noise signal in the vehicle;
the response rejection module is used for rejecting to respond to the call trigger signal when the vehicle is in a running state;
the feature value extraction module includes:
the noise signal segment extraction submodule is used for extracting a continuous noise signal segment of which the amplitude is within a preset range from the in-vehicle noise signal;
the frequency spectrum acquisition submodule is used for carrying out Fourier transform on the continuous noise signal segment to obtain the frequency spectrum of the continuous noise signal segment;
the power spectral density acquisition sub-module is used for acquiring the preset octave power spectral density of the continuous noise signal segment according to the frequency spectrum of the continuous noise signal segment, wherein the characteristic value comprises the frequency spectrum of the continuous noise signal segment and the preset octave power spectral density;
the driving state determination module includes:
and the running state determining submodule is used for determining that the vehicle is in a running state when the frequency spectrum of the continuous noise signal section meets a preset condition and the preset octave power spectral density of the continuous noise signal section is smaller than a preset power spectral density threshold value, wherein the preset condition is that the frequency spectrum is in a linear descending trend from low frequency to high frequency.
4. The apparatus of claim 3, further comprising:
the training sample acquisition module is used for acquiring in-vehicle noise signals of different vehicles in different driving states as training samples;
the training sample analysis module is used for carrying out time domain and frequency domain analysis on the training sample to obtain the frequency spectrum and the preset power spectral density of the training sample;
and the setting module is used for setting the preset condition according to the frequency spectrum of the training sample and setting the preset power spectral density threshold according to the preset octave power spectral density of the training sample.
5. A call control device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the call control method of claim 1 or 2.
6. A computer-readable storage medium, on which computer program instructions are stored, which program instructions, when executed by a processor, carry out the steps of the method of claim 1 or 2.
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| CN114519928B (en) * | 2020-09-25 | 2023-08-15 | 阿波罗智联(北京)科技有限公司 | Method, device, vehicle, electronic device and medium for controlling vehicle |
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| JP2009206629A (en) * | 2008-02-26 | 2009-09-10 | Sony Corp | Audio output device, and audio outputting method |
| CN101980336B (en) * | 2010-10-18 | 2012-01-11 | 福州星网视易信息***有限公司 | Hidden Markov model-based vehicle sound identification method |
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