CN115474121A - Active noise reduction method, device, chip, earphone and storage medium - Google Patents
Active noise reduction method, device, chip, earphone and storage medium Download PDFInfo
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Abstract
The application discloses an active noise reduction method, an active noise reduction device, a chip, an earphone and a storage medium, and relates to the technical field of audio processing. The method comprises the following steps: the first processor determines a target active noise reduction mode according to the noise characteristics of the environment where the earphone is located and generates filter parameters; the second processor performs active noise reduction processing based on the filter parameters provided by the first processor. According to the embodiment of the application, the ANC mode is intelligently determined based on the noise characteristics of the environmental audio through the double processors, and the quality of active noise reduction of the environmental noise in different scenes is improved.
Description
Technical Field
The embodiment of the application relates to the technical field of audio processing, in particular to an active noise reduction method, an active noise reduction device, an active noise reduction chip, an earphone and a storage medium.
Background
With the development of audio technology, compared with passive noise reduction based on physical means, active noise reduction has become the mainstream noise reduction mode of earphone equipment. The Active Noise reduction earphone collects ambient Noise through a microphone, determines a Noise signal by using a filter in an ANC (Active Noise Control) structure, plays an anti-Noise signal which is matched with the amplitude of the Noise signal and has an opposite phase through a loudspeaker, and reduces Noise through Noise cancellation.
In the related art, an active noise reduction earphone determines an ANC mode according to control of a terminal device or default setting of the active noise reduction earphone, and in the fixed ANC mode, the earphone device can only achieve a noise reduction effect through a filtering mode, for example, all environmental audio is eliminated, or only human voice is kept, and when an environment where a user is located changes, the earphone device cannot flexibly adapt to an environmental noise characteristic adjustment filtering mode, and the requirement of the user for the noise reduction effect when the user switches a use scene is difficult to meet.
Disclosure of Invention
The embodiment of the application provides an active noise reduction method, an active noise reduction device, a chip, an earphone and a storage medium, and an ANC mode can be intelligently determined based on noise characteristics through a dual processor (DSP), so that the quality of active noise reduction of environmental noise in different scenes is improved. The technical scheme is as follows:
in one aspect, an embodiment of the present application provides an active noise reduction method, where the method is used for a headset, where the headset includes a first processor and a second processor, and the method includes:
the first processor determines a target active noise reduction mode according to the noise characteristics of the environment where the earphone is located and generates filter parameters;
the second processor performs active noise reduction processing based on the filter parameters provided by the first processor.
In another aspect, an embodiment of the present application provides an active noise reduction apparatus, where the apparatus includes:
the first processor is used for determining a target active noise reduction mode according to the noise characteristics of the environment where the earphone is located and generating filter parameters;
and the second processor is used for carrying out active noise reduction processing based on the filter parameters provided by the first processor.
In another aspect, an embodiment of the present application provides a chip, where the chip includes a first processor and a second processor, and the first processor is configured to:
determining a target active noise reduction mode according to the noise characteristics of the environment where the earphone is located and generating filter parameters;
the second processor is configured to:
and performing active noise reduction processing based on the filter parameters provided by the first processor.
In another aspect, an embodiment of the present application provides a headset, which includes a processor and a memory, where the processor includes at least a first processor and a second processor, and the memory stores at least one program, and the at least one program is loaded and executed by the processor to implement the active noise reduction method according to the above aspect.
In another aspect, an embodiment of the present application provides a computer-readable storage medium, where at least one program is stored, and the at least one program is loaded and executed by a processor to implement the active noise reduction method according to the foregoing aspect.
In another aspect, embodiments of the present application provide a computer program product including computer instructions, which are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the active noise reduction method according to the above aspect.
In the embodiment of the application, the earphone device realizes the noise reduction function through the dual processors, wherein the first processor determines a more appropriate target active noise reduction mode and generates corresponding filter parameters in a plurality of active noise reduction modes based on the noise characteristics of the environment, and the second processor carries out active noise reduction processing based on the filter parameters; according to the scheme, by analyzing the noise characteristics of the environmental audio, the target noise reduction mode meeting the user requirement is intelligently determined in the active noise reduction modes suitable for different noise environments, and compared with the prior art, only one active noise reduction mode is fixedly adopted for noise reduction treatment, so that the quality of actively reducing the noise of the environmental noise in different scenes is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a block diagram illustrating a structure of a headset according to an exemplary embodiment of the present application;
FIG. 2 illustrates a flow chart of an active noise reduction method provided by an exemplary embodiment of the present application;
FIG. 3 illustrates a schematic diagram of determining target active noise reduction provided by an exemplary embodiment of the present application;
FIG. 4 illustrates a flow chart of an active noise reduction method provided by another exemplary embodiment of the present application;
FIG. 5 illustrates a process diagram of first processor noise processing provided by an exemplary embodiment of the present application;
FIG. 6 is a block diagram illustrating a noise reduction pattern for a feedforward criteria provided by an exemplary embodiment of the present application;
FIG. 7 is a block diagram illustrating a hybrid standard noise reduction mode provided by an exemplary embodiment of the present application;
FIG. 8 is a block diagram illustrating an adaptive noise reduction mode provided by an exemplary embodiment of the present application;
FIG. 9 is a block diagram illustrating a directional noise reduction mode provided by an exemplary embodiment of the present application;
FIG. 10 illustrates a process diagram of second processor noise processing provided by an exemplary embodiment of the present application;
FIG. 11 illustrates a flow chart of a directional noise reduction method provided by an exemplary embodiment of the present application;
fig. 12 illustrates a system block diagram of beamforming provided by an exemplary embodiment of the present application;
fig. 13 illustrates a system diagram of beamforming provided by an exemplary embodiment of the present application;
fig. 14 shows a block diagram of an active noise reduction device according to an exemplary embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, the following detailed description of the embodiments of the present application will be made with reference to the accompanying drawings.
For convenience of understanding, terms referred to in the embodiments of the present application will be described below.
Active Noise Control (ANC): active noise reduction is called as active noise reduction for short, and is a mode for reducing the influence of external noise on the using effect of the earphone. All include the adapter, handle chip, three kinds of components and parts of speaker in the equipment that possesses the initiative and fall the function of making an uproar, equipment passes through the adapter, for example microphone, acquires the noise to confirm the wave form characteristics of noise through handling the chip, further calculate the wave of anti-phase with the noise wave form based on noise wave form processing chip, as anti-noise signal, and then play this anti-noise signal through the speaker. When the ambient noise or the built-in noise and the anti-noise signal are simultaneously transmitted to the human ear, the noise signal and the anti-noise signal in phase opposition to the noise signal are cancelled out, thereby achieving noise reduction. The active noise reduction has a good processing effect on low-frequency noise, and compared with a passive noise reduction mode, a user can isolate external noise without increasing audio playing volume, and the damage to human ears is reduced while noise reduction is realized.
Beamforming (Beamforming): also known as beamforming, spatial filtering, is a signal processing technique for transceiving radio waves or sound waves through directional signals, and is commonly used in radar, sonar systems, wireless communication, acoustics, and biomedical devices. In the field of audio processing, a beam former (Beamformer) performs constructive interference on signals at certain angles (target directions) and performs destructive interference on signals at other angles (non-target directions) by adjusting basic unit parameters of a phased array based on the determination of a sound source. And further, carrying out weighted summation and filtering on signals output by each microphone in the microphone array, namely, carrying out combination processing on multiple paths of microphone signals, and finally outputting an audio signal in a desired direction.
Referring to fig. 1, a block diagram of a headset according to an exemplary embodiment of the present application is shown. The headset 100 may include one or more of the following components: audio subsystem (Audio SS) 110, codec (Codec) 120.
The audio subsystem 110 may include a Digital Signal Processor (DSP) 111, a Serial Peripheral Interface (SPI) 112, an Integrated Circuit (IC) built-in audio bus 113 (I2S), and a Gyro sensor (Gyro) 114. The DSP111 is used to process the audio, such as performing different formats of codec, sound processing, audio playing, recording, and the like on the audio. SPI112 comprises a master module and one or more slave modules, and exchanges data by the master module selecting one of the slave modules for synchronous communication. The gyro sensor 114 is used to detect the head posture of the user.
Codec 120 may include a conventional Digital Signal processor 121 (TDSP), a high speed Digital Signal processor 122 (FDSP), a Serial Peripheral Interface (SPI), an Inter-IC Sound 124 (I2S), and a microphone assembly 125. The TDSP and the FDSP jointly realize an active noise reduction function, the TDSP can analyze the noise characteristics of the environmental audio and determine a scene corresponding to noise based on strong calculation power, and corresponding filter parameters are sent to the FDSP by the TDSP under the condition that the filter parameters are determined based on the noise characteristics, and efficient noise filtering is carried out by the FDSP. The microphone assembly 125 may include a feedforward microphone located outside the headset for capturing external ambient audio, the microphone 125 may also include a feedback microphone located inside the headset for capturing audio at the human ear, and optionally, the microphone assembly 125 may be a voice microphone.
In addition, those skilled in the art will appreciate that the configuration of the headset 100 shown in the above figures does not constitute a limitation of the headset, and that the headset may include more or fewer components than shown, or some components may be combined, or a different arrangement of components. For example, the headset 100 further includes a sound unit, a speaker, a sensor (such as an acceleration sensor, an angular velocity sensor, a light sensor, etc.), an audio circuit, a Wi-Fi (Wireless Fidelity, wireless communication technology) module, a power supply, a bluetooth module, etc., which are not described herein again.
Referring to fig. 2, a flowchart of an audio signal processing method according to an exemplary embodiment of the present application is shown. The embodiment of the present application is described by taking the method as an example for the earphone device shown in fig. 1, and the method may include the following steps.
In step 201, a first processor determines a target active noise reduction mode according to noise characteristics of an environment where the headset is located and generates filter parameters.
Wherein the first processor may be a TDSP with a strong computational power. In a possible implementation, the headset acquires the ambient audio through a feed-forward microphone, and further, the first processor performs noise analysis on the ambient audio to determine the noise characteristics of the current environment where the user is located. For the environmental audio with different noise characteristics, the noise reduction requirements of users also have a certain difference, and further for different noise environments, the first processor determines to be suitable for different active noise reduction modes, that is, the first processor determines a target active noise reduction mode based on the noise characteristics.
Illustratively, as shown in fig. 3, the first processor performs real-time noise analysis on the ambient audio, and then selects an appropriate active noise reduction mode from a standard noise reduction mode, an adaptive noise reduction mode, and a directional noise reduction mode as a target active noise reduction mode based on the noise characteristics obtained by the analysis processing.
Further, in different active noise reduction modes, the first processor determines the filter parameters in different manners, and the filter parameters in the different active noise reduction modes have different characteristics correspondingly. In an embodiment of the application, the first processor determines a target active noise reduction mode based on the noise characteristics, i.e. determines filter parameters matching the ambient audio.
In another possible implementation manner, in the case that the noise environment where the user is located changes, the first processor may intelligently switch the target active noise reduction mode to perform active noise reduction processing based on the noise characteristics obtained by the noise analysis. Illustratively, when a user is in an office environment, the first processor determines a noise reduction mode suitable for canceling all noises as a target active noise reduction mode based on the noise characteristic of single noise in the office, when the user finishes working and enters a subway, the ambient audio is noisy and specific scene noise exists in the subway, such as a station arrival prompt tone, and based on the change of the noise characteristic, the first processor can intelligently switch the target active noise reduction mode into a noise reduction mode with the effect of filtering specific sound.
Optionally, the user may adjust the active noise reduction mode by himself or herself in a manual configuration manner, and illustratively, the user may configure the active noise reduction mode through an APP (Application) having an active noise reduction function, or adjust the active noise reduction mode through a predefined gesture, such as a tap.
In step 202, the second processor performs active noise reduction processing based on the filter parameters provided by the first processor.
Wherein the second processor may be an FDSP having a faster signal processing speed. In a possible implementation manner, the first processor sends a filter parameter to the second processor, and the second processor performs inverse processing on the environmental audio according to the curing logic based on the filter parameter, so as to obtain an inverse audio with the same amplitude as the environmental audio and with an opposite phase to the environmental audio, thereby implementing active noise reduction processing.
In summary, in the embodiment of the present application, the earphone implements an active noise reduction function through dual processors, where a first processor determines a noise characteristic of an environment, determines a target active noise reduction mode meeting a noise reduction requirement and generates corresponding filter parameters in a plurality of active noise reduction modes, and a second processor performs active noise reduction processing based on the parameters when receiving the filter parameters; according to the scheme, by analyzing the noise characteristics of the environmental audio, the target noise reduction mode meeting the user requirement is intelligently determined in a plurality of active noise reduction modes suitable for different noise environments, and compared with the prior art, only one active noise reduction mode is fixedly adopted for noise reduction treatment, so that the quality of actively reducing the noise of the environmental noise in different scenes is improved.
In the embodiment of the application, the earphone devices respectively utilize the dual processors to actively reduce noise. Referring to fig. 4, a flow chart of an active noise reduction method is shown. The embodiment of the present application is described by taking the method as an example for the earphone device shown in fig. 1, and the method may include the following steps.
Step 401, a first processor preprocesses an environmental audio to obtain frequency spectrum information of the environmental audio.
In the case where the feedforward microphone collects ambient audio, the ambient audio is transmitted backwards in PCM (Pulse Code Modulation) data segments through digital-to-analog conversion and is subjected to noise analysis by a noise engine in the first processor.
In one possible implementation, as shown in fig. 5, when obtaining the PCM data segment of the ambient audio transmitted by the feedforward microphone, the first processor performs real-time preprocessing on the PCM data segment and stores the preprocessed spectral information segment in the buffer. In the preprocessing process, the waveform of the environmental audio in the time domain is expressed based on the PCM data segment, the first processor obtains the environmental audio expressed by the combination of a plurality of sine waves in a Fourier transform mode and the like, namely obtains the expression of the environmental audio in the frequency domain, and further obtains the frequency spectrum information of the corresponding PCM data segment. The frequency spectrum information represents acoustic characteristics such as frequency and amplitude of the environmental audio.
At step 402, the first processor extracts noise parameters characterizing noise from the spectral information.
In one possible implementation, as shown in fig. 5, the first processor extracts, based on the spectrum information, noise-related vector model parameters including acoustic features such as frequency and amplitude that can characterize the noise characteristics through an algorithmic analysis library (Algo Lib). Optionally, the first processor may further analyze the spectrum information through a Neural Networks (NN) model to obtain the noise parameter.
In step 403, the first processor determines a target active noise reduction mode from at least two active noise reduction modes based on the noise parameters.
The noise parameters represent the noise characteristics of the environmental audio, namely the environmental characteristics of the noise environment where the user is located, and for different noise environments, the requirements of the user on the noise reduction effect are different, so that the first processor can determine the noise reduction requirements of the user based on the noise characteristics, namely, determine the corresponding target active noise reduction mode.
In one possible implementation, as shown in fig. 5, the first processor compares the obtained noise parameters with vector models in a database of Data models (Data Lib), and determines one of at least two active noise reduction modes that meets the characteristics of the ambient audio noise as a target active noise reduction mode.
Specifically, the first processor matches the noise parameters with noise models corresponding to different active noise reduction modes to determine a target active noise reduction mode, wherein the matching degree of the noise parameters with the noise models corresponding to the target active noise reduction mode is higher than the matching degree of the noise parameters with the noise models corresponding to other active noise reduction modes.
In one possible implementation, the active noise reduction mode may include at least two of a standard noise reduction mode, an adaptive noise reduction mode, and a directional noise reduction mode. The standard noise reduction mode can realize all-around noise reduction, namely all noises in the environment are eliminated as much as possible, and based on the characteristic, the standard noise reduction mode has higher matching degree under the conditions that the noise parameter expression is single and no special frequency spectrum appears. For example, when the user uses the active noise reduction function in the office, the ambient noise in the office is mostly the steady and disordered sounds such as white noise, and the user does not have the requirement of acquiring special ambient noise, and the active noise reduction can be realized by eliminating all ambient audio on the premise of ensuring the user requirement.
Correspondingly, the adaptive noise reduction mode can determine a current noise scene based on noise parameters of the environmental audio and the like, further determine that the user has the required environmental audio in the scene, and further adaptively reserve part of special noise while determining to eliminate part of noise. For the condition that the noise parameter expression is disordered and special frequency spectrum irregularity occurs, the adaptive noise reduction mode has higher matching degree. For example, when a user uses an active noise reduction function in a subway, the noise of the ambient noise in the subway changes rapidly in disorder, and the arrival prompt tone and the like which need to be acquired by the user appear randomly, in the adaptive noise reduction mode, the earphone device can retain the special ambient noise such as the arrival prompt tone and the like based on a scene while filtering the noisy bottom noise, so as to meet the user use requirement, that is, the user can acquire the special noise of a specific type through the adaptive noise reduction mode.
In the directional noise reduction mode, the earphone device may determine directions of the sound sources based on information such as sound pressure difference, time difference, and the like of sound received by the microphone array, and when it is detected that noise from a specific direction continues for a certain time, the directional noise reduction mode may determine that the noise is specific noise that a user may acquire a demand possibility, and further selectively retain the specific noise in a noise reduction process. For the case that a specific noise exists in the noise parameters continuously, the directional noise reduction mode has a higher matching degree. For example, when the user uses the active noise reduction function in a factory environment, the conversation sound of the fellow worker comes from a certain fixed direction of the user and continues for a certain time, and the directional noise reduction mode can reserve the noise on the basis of determining the direction of the conversation sound source of the fellow worker, that is, the user can obtain special noise with a specific direction through the directional noise reduction mode.
404, generating a first filter parameter by the first processor according to a working scene of the earphone; and/or generating second filter parameters from ambient audio for at least one sound source direction of a sound source received by the headphones.
Based on different noise characteristics, namely different environmental audios, the earphone adopts different target active noise reduction modes, and in the noise reduction process, each active noise reduction mode adopts different filter parameters to realize corresponding noise reduction effect. The manner of performing noise filtering may be any of the following manners:
1. and generating a first filter parameter according to the working scene of the earphone.
1.1, under the condition that a target active noise reduction mode is a self-adaptive noise reduction mode, determining a working scene of the earphone based on the environmental audio; first filter parameters are generated according to the working scene. Wherein different working scenarios correspond to different filter parameters.
The structure of the adaptive noise reduction mode is shown in fig. 8, the earphone first acquires ambient noise x (t) through a feedforward microphone to obtain ambient audio x (n), then the first processor performs noise analysis on the ambient audio and determines the noise characteristics of the ambient audio, and based on that the ambient audio is noisy and contains random specific noise, the first processor performs active noise reduction by using the adaptive noise reduction mode. The specific noise needing to be reserved is determined according to the noise environment based on the adaptive noise reduction mode, and the more accurate working scene is determined through the scene identification unit in the adaptive noise reduction mode, for example, the working scene can be an outdoor scene, a subway scene, a cafe scene and the like. Based on a working scene, the first processor acquires corresponding filter parameters, and the first filter parameters are different in different scenes based on the difference of frequencies and the like of noise needing to be reserved.
In the case that the earphone is provided with a feedback microphone, as shown in fig. 7, the feedback microphone collects a feedback audio e (n), the feedback filter performs feedback filtering based on the feedback audio, and in the adaptive noise reduction mode, the first processor correspondingly obtains a second target filtering parameter applied to the feedback filter and sends the second target filtering parameter to the feedback filter in the second processor.
Optionally, because the environmental audio frequency becomes obvious in time varying in the noisy environment, better filtering effect can be achieved later by adopting variable filter parameters, and then the adaptive noise reduction mode can be combined with an adaptive algorithm to improve the active noise reduction effect in the noisy environment. For example, the adaptive noise reduction mode may employ a Least Mean Square (LMS) algorithm to periodically adjust filter parameters based on feedback audio collected by the feedback microphone to reduce the error between the target noise and the determined noise. In the active noise reduction process, the least mean square algorithm can be realized by the following calculation process:
y(k)＝w T (k)x(k)
e(k)＝d(k)-y(k)
w(k+1)＝w(k)+μe(k)x(k)
where y (k) is the desired output, w T (k) Representing the filter coefficient vector in the kth iteration, x (n) being the inputThe input signal, i.e. the vector of N values collected in the most recent time segment, e (k) represents the noise error between the target noise d (k) and the desired output y (k), and is a special gradient descent calculation based on the least mean square algorithm, where μ is the step size of the gradient descent, and w (k + 1) is the filter coefficient vector in the (k + 1) th iteration. The calculation process is calculated by an iterative loop, so that y (n) is as close to d (n) as possible, i.e. the error e (n) is as small as possible.
2. The second filter parameters are generated from the ambient audio of at least one sound source direction of the sound source received by the headphones.
2.1, under the condition that the target active noise reduction mode is the standard noise reduction mode, generating second filter parameters according to the environment audios of all sound source directions received by the earphone.
The standard noise reduction mode is configured as shown in fig. 6, and the earphone acquires external noise x (t) through a feedforward microphone to obtain an ambient audio x (n). In the process of transmitting the external noise to the human ear through the earphone, i.e. in the main sound path through the earphone housing and the cavity, the sound is attenuated in the transmission process, and the attenuated noise audio obtained at the human ear is x' (t), and the attenuation can be represented by the main sound transfer function P (n). The first processor performs a noise analysis on the ambient audio x (n) and determines a noise parameter characterizing its noise characteristics. Based on the single and unordered noise reduction parameters, the first processor determines the standard noise reduction mode as a target active noise reduction mode, and in this mode, the first processor may determine the second filter parameter based on the omni-directional ambient audio on the basis of receiving the ambient audio from each sound source direction because the ambient audio is unordered and stationary. Under the above circumstances, the first processor may obtain a better noise reduction effect by using the predefined filter parameter, that is, the first processor obtains the fixed filter parameter corresponding to the standard noise reduction mode as the second filter parameter, for example, the white noise filtering parameter, and sends the second filter parameter to the second processor.
In a possible embodiment, a feedback microphone may be further provided in the headset, and the feedback microphone is used for collecting feedback audio. Under the above situation, the first processor sends a first target filtering parameter and a second target filtering parameter to the second processor based on the target active noise reduction mode, where the first target filtering parameter is a parameter used by the feedforward filter in the second processor to perform audio filtering, and the first target filtering parameter is a parameter used by the feedback filter in the second processor to perform audio filtering. The first target filtering parameter and the second target filtering parameter both belong to the filter parameters. As shown in fig. 7, in the case of having a feedback microphone, the feedback microphone collects feedback audio e (n) at the ear of a person, and the first processor performs noise analysis on the feedback audio to determine a second target filtering parameter and sends the second target filtering parameter to the feedback filter in the second processor for feedback filtering.
2.2, under the condition that the target active noise reduction mode is the directional noise reduction mode, determining the direction of a target sound source according to the environmental audio received by the earphone; second filter parameters are generated from the ambient audio in directions other than the target sound source direction.
The structure of the directional noise reduction mode is shown in fig. 9, and compared with the standard noise reduction mode in which noise reduction processing is performed only by a feedforward filter or by combining with a feedback filter, the directional noise reduction mode introduces a directional noise reduction unit to assist in determining specific noise that needs to be retained. Based on the fact that the first processor adopts a directional noise reduction mode under the condition that continuous special noise with a specific direction exists in the environment audio, the corresponding noise environment has certain time variability, and parameters of a second filter can be determined by combining self-adaptive algorithms such as LMS (least mean square) and the like in the noise reduction mode to optimize the noise reduction effect. The first processor may determine, based on the ambient audio, that the continuous noise direction having the characteristic audio information is a target sound source direction, and optionally, in the directional noise reduction mode, the first processor may obtain, based on the mode, a corresponding predefined filter parameter as a second filter parameter, and optionally, further optimize the second filter parameter based on the ambient audio in a direction other than the target sound source direction.
In step 405, the second processor configures a filter based on the filter parameters and performs filtering processing on the environmental audio through the filter.
The filter parameter may be the first filter parameter or the second filter parameter. The second processor may be an FDSP, and in a possible implementation, the FDSP may include a plurality of filters, and in a case where the earphone is provided with a feedback filter, the plurality of filters may simultaneously filter the ambient audio collected by the feedforward microphone and the feedback audio collected by the feedback microphone.
The process of performing filtering processing by the second processor based on the filter parameters is described by taking the adaptive noise reduction mode as an example of the earphone, and as shown in fig. 8, the second processor determines a filter coefficient vector W (the vector W is expressed as W1 (z) in the frequency domain) suitable for the feedforward filter based on the filter parameters sent by the first processor, and the feedforward filter determines a desired output y (n) based on the filter coefficient vector W and the input signal x (n), that is, the ambient audio.
At step 406, the second processor generates an inverse audio based on the filtered ambient audio, the inverse audio having an opposite phase to the filtered ambient audio.
In one possible implementation, as shown in fig. 10, when obtaining the PCM (audio data) of the environmental audio, the second processor first pre-processes the audio and performs filtering based on the filter parameters to obtain an inverse audio with the same amplitude and opposite phase as the environmental audio, and then performs cancellation by the inverse audio and the environmental audio to realize active noise reduction.
In the case of the earphone having the feedback microphone, taking the adaptive noise reduction mode as an example, as shown in fig. 8, the feedforward filter and the feedback filter both generate inverse audio frequencies, and in the adaptive noise reduction process, the inverse audio frequencies with higher accuracy can be determined by combining the accumulation operation and the two digital audio signals.
In step 407, the second processor transmits the inverted audio to the speaker to cause the speaker to play the inverted audio.
In the event that inverse audio is determined, the second processor transmits the inverse audio to the speaker. In one possible implementation, taking the adaptive noise reduction mode as an example, as shown in fig. 8, the inverted audio is converted from a digital signal to an analog signal through a digital-to-analog conversion process and transmitted to an AMP (Amplifier), and the AMP gains the analog signal. And then the reversed-phase audio y (n) is played by a loudspeaker (Speaker), the base sound is transmitted to the human ear from the Speaker to be subjected to a secondary sound filter, the sound is attenuated to a certain extent, the attenuation process can be represented by a secondary sound transfer function S (z), the attenuated audio is y ' (t), and when the sound played by the Speaker and the ambient noise are simultaneously transferred to the human ear, the audio with equal amplitude and opposite phase in x ' (t) and y ' (t) is cancelled by interference, so that active noise reduction is realized.
To sum up, in the embodiment of the present application, a dual processor is used for processing noise, in the dual processor, a first processor may be a TDSP capable of flexibly controlling an operating program thereof, and further, in the present application, a first processor is used for performing noise analysis on an environmental audio frequency, determining a noise parameter, and intelligently selecting an active noise reduction mode with a higher degree of matching with a user requirement based on the noise parameter, so as to achieve intelligent picking-free, a further first processor determines a filter parameter suitable for the user noise reduction requirement, and sends the parameter to a second processor, the second processor may be an FDSP having a fixed operation logic and a faster operation speed, the second processor obtains an inverse audio frequency of the environmental audio frequency through a filter based on the filter parameter, and a further speaker plays the inverse audio frequency to achieve cancellation interference, thereby achieving active noise reduction adapted to the user requirement; according to the method and the device, the active noise reduction mode is intelligently determined through noise analysis, and the quality of active noise reduction of the environmental noise in different scenes is improved.
In a special case, a special noise with a fixed sound source position and a long sound duration exists in the environment, the environment sound has a high probability of being the special noise that needs to be heard by the user, and in this case, the earphone in the scheme adopts a directional noise reduction mode to assist the user in hearing based on the noise characteristics. Referring to fig. 11, a flow chart of a directional noise reduction mode provided by an exemplary embodiment of the present application is shown.
In an embodiment of the application, the headset is provided with a plurality of feed-forward microphones, the different feed-forward microphones being arranged to receive ambient audio in different directions. Optionally, the feedforward microphone may be a voice microphone, and a plurality of voice microphones form a microphone array, and in the microphone array, a topological structure of the plurality of microphones may be a linear arrangement or a honeycomb arrangement, and the present solution is not limited to this.
In step 1101, the first processor performs beamforming processing based on the ambient audio by using a beamformer to obtain directional audio, where the beamforming processing is used to generate an audio compensation signal in a target sound source direction to cancel the ambient audio in at least one sound source direction outside the target sound source direction.
As shown in fig. 9, a directional unit 901 is disposed in the directional noise reduction mode, where the directional unit includes multiple microphones for collecting multiple channels of environmental audio, and a beam former (Beamformer) for performing beamforming on multiple channels of environmental audio. In one possible embodiment, as shown in fig. 12, the left and right earphones have two codecs, namely a left codec 1210 and a right codec 1220, respectively, wherein besides TDSPs 1211 and 1221 and FDSPs 1212 and 1222, respectively, three microphones for collecting the ambient audio are respectively disposed in the de-encoder, so as to form microphone arrays 1213 and 1223. Further, as shown in fig. 13, when the left and right earphones are worn, the two earphones monitor the ambient noise within 180 ° on each side by the corresponding microphone arrays. It should be noted that, the method for performing information interaction between the left and right earphones is not limited, and correspondingly, the method does not limit the number of microphones for performing ambient audio collection in the left and right earphones.
For multiple paths of environmental audio, the first processors in the left earphone and the right earphone respectively determine the orientation of a sound source corresponding to the environmental audio based on the time difference, the sound pressure difference and the like of each path of environmental audio reaching the microphone array through a beam forming algorithm. The left earphone and the right earphone can exchange information through a Bluetooth link, so that the two earphones form a beam forming array, sound source information obtained by the two earphones is integrated, and the earphone equipment preliminarily determines that the user has special noise with the possibility of obtaining the special noise through beam forming. And combining other noises in the environmental audio, combining the multiple paths of environmental audio by the first processor, determining the directional audio, and enhancing the environmental audio in the direction of the target sound source based on the directional audio. Correspondingly, the beam former generates the audio compensation signal through the beam forming processing, and a second processor in the following process can weaken noise in the environmental audio in the other directions based on the audio compensation signal, so that the environmental noise required to be acquired by the user is reserved and enhanced while the bottom noise is filtered.
Optionally, the user may set, through the terminal-side application program, an environmental noise type that needs to be determined as a special noise, and an exemplary user may set selection of a specific voice, selection of a specific type of voice, or adaptive selection. Correspondingly, the user can collect specific voice or specific voice by himself and store the frequency spectrum information of the corresponding voice.
Illustratively, when a person in the environment speaks continuously at the left side of the user, the sound pressure detected by the microphone in the microphone array close to the direction of the voice audio source is greater than the sound pressure detected by the microphone in the direction of the sound source, and the sound is continued for a certain period of time or meets predefined special audio acoustic characteristics (pre-collected and stored spectrum information), the directional unit may determine that the sound from the direction is special noise. Furthermore, the first processor determines that the sound source directional noise is enhanced and the corresponding rest directional noise is suppressed in the process of combining the multi-path environment audio.
At step 1102, a first processor sends directional audio to a second processor.
In the case where directional audio is determined, the first processor transmits the directional audio to the second processor so that the second processor performs a filtering process using the directional audio as an input signal.
At step 1103, the second processor generates target audio based on the inverse audio and the directional audio.
Based on the directional audio, the second processor respectively determines a special noise audio from a specific direction and the rest of the noise audio through the filter, and respectively generates an audio signal that constructively interferes with the special noise audio to enhance the special noise audio and an audio signal that destructively interferes with the rest of the noise audio to cancel the rest of the noise audio, thereby realizing active noise reduction while preserving the special noise.
As shown in fig. 9, based on the directional noise reduction mode, selective noise reduction is realized by combining ANC and beamforming, the second processor obtains the inverse audio through the ANC process, and performs an accumulation operation on the obtained audio after the directional audio filtering process and the inverse audio to obtain the target audio.
In step 1104, the second processor transmits the target audio to the speaker to cause the speaker to play the target audio.
This step is the same as step 407, and is not described herein again.
In one possible embodiment, a sensor is provided in the headset. In the case where a specific direction is determined, the first processor determines a posture change parameter of the headphone based on the acquired sensor data, the sensor data including at least a vector rotation angular acceleration of the headphone.
The sensor can be a gyroscope sensor, based on beam forming, a user can acquire environmental noise from a specific direction, when the head of the user rotates, the earphone can determine the rotating direction and the rotating angle of the head of the user through data of the gyroscope sensor, and further based on the data, the earphone can determine the reflecting condition of the user to the acquired environmental noise, and further accurate positioning can be carried out on special noise by the first processor based on attitude change parameters of the earphone, so that the noise reduction accuracy of a directional noise reduction mode is improved. Wherein the attitude change parameters at least comprise the rotation moment, the rotation amplitude and the rotation direction.
In a possible implementation mode, under the condition that the auxiliary positioning function of the gyroscope is started, the earphone acquires gyroscope sensor data in real time based on the sampling frequency f, and the gyroscope sensor data contains vector angular acceleration values b in the directions of three axes of X, Y and Z t (x, y, z). Setting key value pair k t ＝(r t ,b t ) Wherein r is t Is the sampling time, b t Is vector angular acceleration value and is based on a set time window of T secondsObtaining the number of N T And stored in a circular queue in data structure K [ N ] T ]In which N is T = T × f and have
In the directional noise reduction mode, when the first processor preliminarily detects special noise, the gyroscope sensor data is obtained based on the earphone, and the first processor searches for the key value pair from the moment of receiving the special noise to perform tracking and calculation.
Illustratively, when the user uses the active noise reduction function in a factory environment, the worker speaks continuously in the direction of 30 ° at the left front side of the user, and through preliminary beamforming, the first processor determines that the range of 45 ° at the left front side of the user is the special noise source direction when combining multiple channels of environmental audio, and enhances the environmental noise from the range to weaken the environmental noise from the other directions. When the user hears the conversation sound and turns his head toward the fellow worker, the gyroscope sensor data represents the direction and amplitude of the turning of the head of the user, and can further determine the direction of the voice audio from the fellow worker as the user facing direction, i.e., the direction of 30 degrees from the front left of the user.
Further, the first processor modifies the specific direction based on the attitude change parameter. And, in a case where the posture change parameter is larger than the threshold value, the first processor corrects the specific direction based on the posture change parameter.
The earphone can determine that the user reacts to the special noise when the change of the gyroscope sensor data is detected after the moment of receiving the special noise and the attitude change parameter is larger than the threshold value, and the initially determined sound source direction has a certain error.
And then, the first processor carries out beam forming processing on the multi-channel environment audio based on the corrected specific direction to obtain directional audio. In the beam forming process, the first processor further enhances the modified environmental noise from the specific direction, correspondingly further filters the environmental noise from other directions, and improves the directional noise reduction effect.
In summary, the scheme detects the environmental noise in real time through beam forming, and keeps the special noise in the environment in the active noise reduction process, so as to ensure that the user can still obtain the environmental noise with the possibility of obtaining the requirement, such as speaking sound, alarm sound and the like, under the condition of using the active noise reduction function; according to the scheme, on the basis of realizing directional noise reduction through beam forming, the head action of a user is detected by combining sensor equipment such as a gyroscope, the reaction condition of the user to specific noise is determined by utilizing information such as head rotation, the accuracy of special noise positioning is further improved, the active noise reduction effect is improved, and the active noise reduction quality is improved.
Referring to fig. 14, a block diagram of an active noise reduction apparatus provided in an exemplary embodiment of the present application is shown, where the apparatus includes:
a first processor 1401 for determining a target active noise reduction mode according to noise characteristics of an environment in which the headset is located and generating filter parameters;
a second processor 1402 for performing active noise reduction processing based on the filter parameters provided by the first processor.
Optionally, the first processor 1401 is further configured to:
preprocessing an environmental audio to obtain frequency spectrum information of the environmental audio;
extracting noise parameters for characterizing the noise characteristics from the spectral information;
determining the target active noise reduction mode from at least two active noise reduction modes based on the noise parameter.
Optionally, the first processor 1401 is further configured to:
and matching the noise parameters with noise models corresponding to different active noise reduction modes to determine the target active noise reduction mode.
Optionally, the first processor 1401 is further configured to:
generating a first filter parameter according to the working scene of the earphone; and/or
Generating second filter parameters from the ambient audio for at least one sound source direction of a sound source received by the headphones.
Optionally, in a case that the active noise reduction mode includes a standard noise reduction mode, an adaptive noise reduction mode, and a directional noise reduction mode, the first processor 1401 is further configured to:
determining a working scene of the headset based on the environmental audio under the condition that the target active noise reduction mode is the adaptive noise reduction mode; generating the first filter parameter according to the working scene;
generating the second filter parameter according to the environment audio in each sound source direction received by the earphone under the condition that the target active noise reduction mode is the standard noise reduction mode;
determining a target sound source direction according to the environment audio received by the earphone under the condition that the target active noise reduction mode is a directional noise reduction mode; generating the second filter parameter according to the ambient audio of the target sound source direction.
Optionally, the second processor 1402 is further configured to:
configuring a filter based on the filter parameters, and filtering the environmental audio through the filter;
generating an inverse audio based on the filtered ambient audio, wherein the inverse audio and the filtered ambient audio are opposite in phase;
transmitting the inverse audio to a speaker to cause the speaker to play the inverse audio.
Optionally, in a case that the earphone is provided with a plurality of feedforward microphones, and different feedforward microphones are used for receiving the environmental audio in different directions, the first processor 1401 is further configured to:
the first processor performs beamforming processing based on the environmental audio by using a beamformer to obtain directional audio, wherein the beamforming processing is used for generating an audio compensation signal in a target sound source direction to cancel the environmental audio in at least one sound source direction outside the target sound source direction;
the first processor sending the directional audio to the second processor;
the second processor 1402, further configured to:
the second processor generating target audio based on the inverse audio and the directional audio;
the second processor transmits the target audio to the speaker to cause the speaker to play the target audio.
Optionally, in a case that a sensor is disposed in the headset, and the sensor is configured to determine a motion state of the headset, the first processor 1401 is further configured to:
in a case where the target sound source direction is determined, the first processor determines an attitude change parameter of the headphone based on acquired sensor data, the sensor data including at least a vector rotation angular acceleration of the headphone;
the first processor corrects the target sound source direction based on the attitude change parameter;
and the first processor performs the beam forming processing on the multi-channel environment audio based on the corrected target sound source direction to obtain the directional audio.
Optionally, the first processor 1401 is further configured to:
and under the condition that the attitude change parameter is larger than a threshold value, correcting the direction of the target sound source based on the attitude change parameter.
Optionally, in a case that the earphone is further provided with a feedback microphone, and the feedback microphone is configured to collect feedback audio, the first processor 1401 is further configured to:
and sending a first target filtering parameter and a second target filtering parameter to the second processor based on the target active noise reduction mode, wherein the first target filtering parameter is a parameter used by a feedforward filter in the second processor for audio filtering, and the first target filtering parameter is a parameter used by a feedback filter in the second processor for audio filtering.
Optionally, the first processor 1401 is further configured to perform noise reduction processing in parallel with the second processor 1402.
In summary, in the embodiment of the present application, a noise reduction function is implemented by using dual processors, where a first processor determines a more suitable target active noise reduction mode and generates filter parameters in a plurality of active noise reduction modes based on noise characteristics of an environmental audio collected by a feedforward microphone, and a second processor performs active noise reduction processing based on the parameters when receiving the filter parameters; according to the scheme, by analyzing the noise characteristics of the environmental audio, the target noise reduction mode meeting the user requirement is intelligently determined in the active noise reduction modes suitable for different noise environments, and compared with the prior art, only one active noise reduction mode is fixedly adopted for noise reduction treatment, so that the quality of actively reducing the noise of the environmental noise in different scenes is improved.
Embodiments of the present application further provide a chip, where the chip includes a first processor and a second processor, and the first processor is configured to: determining a target active noise reduction mode according to the noise characteristics of the environment where the earphone is located and generating filter parameters; the second processor is configured to: the active noise reduction process described in the above embodiments is performed based on the filter parameters provided by the first processor.
The embodiment of the present application further provides a computer-readable storage medium, in which at least one program is stored, and the at least one program is used for being executed by a processor to implement the active noise reduction method according to the embodiment.
Embodiments of the present application provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to make the computer device execute the active noise reduction method provided by the above embodiments.
Those skilled in the art will recognize that, in one or more of the examples described above, the functions described in the embodiments of the present application may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (16)
1. An active noise reduction method applied to a headphone, the headphone comprising a first processor and a second processor, the method comprising:
the first processor determines a target active noise reduction mode according to the noise characteristics of the environment where the earphone is located and generates filter parameters;
the second processor performs active noise reduction processing based on the filter parameters provided by the first processor.
2. The method of claim 1, wherein the first processor determines a target active noise reduction mode and generates filter parameters based on noise characteristics of an environment in which the headset is located, comprising:
preprocessing an environmental audio to obtain frequency spectrum information of the environmental audio;
extracting noise parameters for characterizing the noise characteristics from the spectral information;
determining the target active noise reduction mode from at least two active noise reduction modes based on the noise parameter.
3. The method of claim 2, wherein determining the target active noise reduction mode from at least two active noise reduction modes based on the noise parameter comprises:
and matching the noise parameters with noise models corresponding to different active noise reduction modes to determine the target active noise reduction mode.
4. The method of claim 2, wherein the first processor determines a target active noise reduction mode and generates filter parameters based on noise characteristics of an environment in which the headset is located, comprising:
generating a first filter parameter according to the working scene of the earphone; and/or
Generating second filter parameters from the ambient audio for at least one sound source direction of a sound source received by the headphones.
5. The method of claim 4, wherein the active noise reduction mode comprises a standard noise reduction mode, an adaptive noise reduction mode, and a directional noise reduction mode;
generating a first filter parameter according to the working scene of the earphone comprises:
determining a working scene of the earphone based on the environmental audio under the condition that the target active noise reduction mode is the self-adaptive noise reduction mode; generating the first filter parameter according to the working scene;
the generating of second filter parameters from ambient audio of at least one sound source direction of a sound source received by the headphones comprises:
under the condition that the target active noise reduction mode is the standard noise reduction mode, generating the second filter parameter according to the environment audio of each sound source direction received by the earphone;
determining a target sound source direction according to the environment audio received by the earphone under the condition that the target active noise reduction mode is a directional noise reduction mode; generating the second filter parameter according to the environmental audio in each direction other than the target sound source direction.
6. The method of claim 1, wherein the second processor performs active noise reduction processing based on the filter parameters provided by the first processor, comprising:
configuring a filter based on the filter parameters, and carrying out filtering processing on the environmental audio through the filter;
generating an inverse audio based on the filtered ambient audio, wherein the inverse audio and the filtered ambient audio are opposite in phase;
transmitting the inverse audio to a speaker to cause the speaker to play the inverse audio.
7. The method of claim 6, wherein the headset is provided with a plurality of feed-forward microphones, different ones of the feed-forward microphones being configured to receive the ambient audio in different directions;
in the case that the target active noise reduction mode is a directional noise reduction mode, the method further comprises:
the first processor performs beamforming processing based on the environmental audio by using a beamformer to obtain directional audio, wherein the beamforming processing is used for generating an audio compensation signal in a target sound source direction to cancel the environmental audio in at least one sound source direction outside the target sound source direction;
the first processor sending the directional audio to the second processor;
the second processor performs active noise reduction processing based on the filter parameters provided by the first processor, and further includes:
the second processor generating target audio based on the inverse audio and the directional audio;
the second processor transmits the target audio to the speaker to cause the speaker to play the target audio.
8. The method of claim 7, wherein a sensor is provided in the headset for determining a state of motion of the headset;
the method further comprises the following steps:
in a case where the target sound source direction is determined, the first processor determines an attitude change parameter of the headphones based on acquired sensor data, the sensor data including at least a vector rotation angular acceleration of the headphones;
the first processor corrects the target sound source direction based on the attitude change parameter;
and the first processor performs the beam forming processing on the multi-channel environment audio based on the corrected target sound source direction to obtain the directional audio.
9. The method of claim 8, wherein the first processor modifies the target sound source direction based on the attitude change parameter, comprising:
and under the condition that the attitude change parameter is larger than a threshold value, correcting the direction of the target sound source based on the attitude change parameter.
10. The method of claim 1, wherein the headset is further provided with a feedback microphone for capturing feedback audio;
the method further comprises the following steps:
and the first processor sends a first target filtering parameter and a second target filtering parameter to the second processor based on the target active noise reduction mode, wherein the first target filtering parameter is a parameter used by a feedforward filter in the second processor for audio filtering, and the first target filtering parameter is a parameter used by a feedback filter in the second processor for audio filtering.
11. The method of claim 1, wherein the first processor and the second processor perform noise reduction processing in parallel.
12. An active noise reduction apparatus, for use in a headset comprising a first processor and a second processor, the apparatus comprising:
the first processor is used for determining a target active noise reduction mode according to the noise characteristics of the environment where the earphone is located and generating filter parameters;
and the second processor is used for carrying out active noise reduction processing based on the filter parameters provided by the first processor.
13. A chip, wherein the chip comprises a first processor and a second processor, wherein the first processor is configured to:
determining a target active noise reduction mode according to the noise characteristics of the environment where the earphone is located and generating filter parameters; the second processor is configured to:
and performing active noise reduction processing based on the filter parameters provided by the first processor.
14. A headset comprising a processor including at least a first processor and a second processor, and a memory in which is stored at least one program that is loaded and executed by the processor to implement the active noise reduction method of any of claims 1 to 11.
15. A computer-readable storage medium, in which at least one program is stored, which is loaded and executed by a processor to implement the active noise reduction method according to any one of claims 1 to 11.
16. A computer program product, characterized in that the computer program product comprises computer instructions, the computer instructions being stored in a computer readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, the processor executing the computer instructions to cause the computer device to perform the active noise reduction method of any of claims 1 to 11.
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