Noise reduction method and computer equipment

Abstract

The invention discloses a noise reduction method and computer equipment, and belongs to the technical field of computers. The method comprises the following steps: acquiring n first audio signals acquired by n sound pickups; determining m groups of noise signals in the n first audio signals, wherein each group of noise signals in the m groups of noise signals comprises n noise signals; determining m sound source positions according to the m groups of noise signals; for any group of noise signals in the m groups of noise signals, determining signal features according to the group of noise signals and the corresponding sound source position, and generating de-noised signals according to the signal features of the group of noise signals; and fusing the n first audio signals and the de-noised signals corresponding to each group of noise signals in the m groups of noise signals to obtain a second audio signal. The noise signal features determined according to the noise signal and the corresponding sound source position can accurately reflect the features of the noise signal, so that the de-noised signal of the noise signal features can effectively eliminate the noise signal, and the noise reduction effect is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a noise reduction method and a computer device. Background Art

[0002] With the continuous advancement of science and technology, noise reduction technology has gradually become popular, among which Environmental Noise Cancellation (ENC) technology is a typical representative. ENC technology collects audio signals through multiple microphones, deeply analyzes the frequency, amplitude, phase and other characteristics of the noise, and then generates a signal opposite to the background noise based on the analysis results, thereby achieving noise reduction. In related technologies, time domain analysis is usually used to extract noise signal characteristics, that is, by observing the waveform changes of the noise signal on the time axis, analyzing its amplitude, period, rise time, fall time and other parameters, so as to determine the characteristics of the noise signal. However, this analysis method often performs poorly in the face of scenes with more complex noise signals, making it difficult to achieve an ideal noise reduction effect. Summary of the invention

[0003] The present application provides a noise reduction method and a computer device, which can improve the noise reduction effect. The technical solution is as follows: In a first aspect, a noise reduction method is provided, the method comprising: Acquire n first audio signals, where the n first audio signals are audio signals collected by n microphones, and n is an integer greater than or equal to 2; Determine m groups of noise signals among the n first audio signals, each group of noise signals among the m groups of noise signals includes n noise signals, and m is a positive integer; Determine m sound source positions according to the m groups of noise signals, wherein the m sound source positions correspond one-to-one to the m groups of noise signals; For any one group of noise signals among the m groups of noise signals, determine signal characteristics of the group of noise signals according to the group of noise signals and sound source positions corresponding to the group of noise signals, and generate denoised signals corresponding to the group of noise signals according to the signal characteristics of the group of noise signals; The n first audio signals are fused with a denoised signal corresponding to each noise signal in the m groups of noise signals to obtain a second audio signal.

[0004] In the present application, after the noise reduction system obtains the n first audio signals collected by the n microphones, m groups of noise signals in the n first audio signals are determined, and each group of noise signals in the m groups of noise signals includes n noise signals. Then, m sound source positions corresponding to the m groups of noise signals are determined. For any group of noise signals in the m groups of noise signals, the signal characteristics of the group of noise signals are determined according to the group of noise signals and the corresponding sound source positions, and the corresponding denoising signals are generated according to the signal characteristics of the group of noise signals. Afterwards, the denoising signals corresponding to the n first audio signals and each group of noise signals in the m groups of noise signals are fused to obtain a second audio signal. In the embodiment of the present application, the noise signal characteristics are determined according to the noise signal and the corresponding sound source positions. The noise signal characteristics can more accurately reflect the characteristics of the noise signal in space. In this way, the denoising signal generated according to the noise signal characteristics can effectively eliminate the noise signal, thereby improving the noise reduction effect.

[0005] Optionally, the determining m groups of noise signals among the n first audio signals includes: For any one of the n first audio signals, input the first audio signal into a first model, and the first model outputs a valid signal and m noise signals in the first audio signal; All noise signals in the n first audio signals are grouped to obtain the m groups of noise signals.

[0006] Optionally, determining the signal characteristics of the group of noise signals according to the group of noise signals and the sound source positions corresponding to the group of noise signals includes: For any one noise signal in the group of noise signals, preprocessing the noise signal and the sound source positions corresponding to the group of noise signals is performed to obtain a group of input data corresponding to the noise signal; n groups of input data corresponding to the group of noise signals are input into a second model, and the second model outputs signal features of the group of noise signals.

[0007] Optionally, the preprocessing of the noise signal and the sound source positions corresponding to the group of noise signals to obtain a group of input data corresponding to the noise signal includes: Segmenting the noise signal to obtain multiple audio segments; Extracting audio features of each audio segment in the plurality of audio segments; The audio features of each audio segment in the multiple audio segments are respectively concatenated with the position features of the sound source positions corresponding to the group of noise signals to obtain multiple target features, where the multiple target features are a group of input data corresponding to the noise signals.

[0008] Optionally, the second model includes a feature extraction model and a fusion module, and the feature extraction model includes a state space model SSM and a transformer model; The SSM is used to output a first signal feature based on the n groups of input data, the transformer model is used to output a second signal feature based on the n groups of input data, and the fusion module is used to output a target signal feature based on the first signal feature and the second signal feature, wherein the target signal feature is the signal feature of the group of noise signals.

[0009] Optionally, the fusion module is used to: concatenate the first signal feature and the second signal feature to obtain a third signal feature; input the third signal feature into a gating model, and the gating model outputs a first weight, where the first weight is the weight corresponding to the SSM; based on the first weight and the second weight, perform weighted fusion on the first signal feature and the second signal feature to obtain the target signal feature, where the second weight is 1 minus the first weight, and the second weight is the weight corresponding to the transformer model.

[0010] Optionally, after fusing the n first audio signals with a denoised signal corresponding to each noise signal in the m groups of noise signals to obtain a second audio signal, the method further includes: Merging all valid signals of the n first audio signals to obtain a third audio signal; determining a signal residual between the second audio signal and the third audio signal; Parameters in the gating model and the feature extraction model are updated according to the signal residual.

[0011] Optionally, the method further comprises: Display a noise setting interface, wherein the noise setting interface includes multiple sound types; In response to receiving a selection operation of at least one sound type among the plurality of sound types, determining the at least one sound type as a noise type; According to the noise type set in the noise setting interface, the first model is adjusted so that the first model is used to output a noise signal of the noise type.

[0012] Optionally, the method further comprises: Displaying a noise reduction mode setting interface, wherein the noise reduction mode setting interface includes one or more noise reduction modes; In response to receiving a selection operation of one of the one or more noise reduction modes, determining at least one sound type corresponding to the selected noise reduction mode as a noise type; According to the noise type set in the noise reduction mode setting interface, the first model is adjusted so that the first model is used to output a noise signal of the noise type.

[0013] In a second aspect, a noise reduction device is provided, the device comprising: An acquisition module, used to acquire n first audio signals, where the n first audio signals are audio signals collected by n pickups, and n is an integer greater than or equal to 2; A first determining module, configured to determine m groups of noise signals among the n first audio signals, each group of noise signals among the m groups of noise signals includes n noise signals, and m is a positive integer; A second determination module is used to determine m sound source positions according to the m groups of noise signals, wherein the m sound source positions correspond one-to-one to the m groups of noise signals; a third determination module, configured to determine, for any one of the m groups of noise signals, signal features of the group of noise signals according to the group of noise signals and sound source positions corresponding to the group of noise signals, and generate a denoised signal corresponding to the group of noise signals according to the signal features of the group of noise signals; The first fusion module is used to fuse the n first audio signals with a denoised signal corresponding to each noise signal in the m groups of noise signals to obtain a second audio signal.

[0014] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the noise reduction method described in the first aspect when executed by the processor.

[0015] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the noise reduction method described in the first aspect is implemented.

[0016] In a fifth aspect, a computer program product is provided. When the computer program product is run on a computer device, the computer device executes the noise reduction method described in the first aspect.

[0017] It can be understood that the beneficial effects of the second, third, fourth and fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a schematic diagram of a noise reduction system provided in an embodiment of the present application; Figure 2 is a diagram of an array structure of a microphone provided in an embodiment of the present application; Figure 3 is a flow chart of a noise reduction method provided in an embodiment of the present application; Figure 4 is a flow chart of a noise identification process provided by an embodiment of the present application; Figure 5 is a flow chart of a noise elimination process provided by an embodiment of the present application; Figure 6 is a structural schematic diagram of a noise reduction device provided in an embodiment of the present application; Figure 7 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0021] It should be understood that the "multiple" mentioned in this application refers to two or more. In the description of this application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in order to facilitate the clear description of the technical solution of this application, the words "first" and "second" are used to distinguish between the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the words "first" and "second" do not limit the quantity and execution order, and the words "first" and "second" do not limit them to be different.

[0022] The phrases such as "one embodiment" or "some embodiments" described in the present application mean that the specific features, structures or characteristics described in the embodiment are included in one or more embodiments of the present application. Therefore, the phrases such as "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments" that appear in the differences in the present application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. In addition, the terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0023] The application scenarios of the embodiments of the present application are described below.

[0024] The embodiments of the present application are applied to the scene of noise reduction, aiming to eliminate environmental noise to improve audio quality. Its application scenes include but are not limited to telephone conferences, audio recording and sound monitoring, etc., which have high requirements on audio clarity.

[0025] In related technologies, active noise reduction (ANC) technology or ENC technology is usually used for noise reduction. ANC technology is based on the principle of "interference reduction". It captures environmental noise through a microphone, converts time domain signals into frequency domain signals, analyzes the frequency distribution of noise, and extracts the spectrum characteristics of audio signals to obtain noise characteristics. ENC technology extracts noise signal characteristics through time domain analysis, that is, observes the waveform changes of the noise signal on the time axis, analyzes its amplitude, period, rise time, fall time and other parameters, and then determines the noise characteristics. However, this noise feature analysis method is often difficult to accurately analyze noise characteristics in the face of a constantly changing and complex environment, resulting in the noise reduction effect not being ideal.

[0026] To this end, an embodiment of the present application provides a noise reduction method, which is applied to a noise reduction system. The method collects multiple audio signals through multiple pickups, identifies multiple groups of noise signals therein, and determines the sound source position corresponding to each group of noise signals. Based on the noise signal and its corresponding sound source position, the noise signal characteristics are analyzed, and a corresponding denoising signal is generated. Since the noise signal characteristics are analyzed in combination with the noise signal and its sound source position, it is possible to more accurately distinguish noise sources in different directions and distances, and perform accurate feature analysis for different noise sources, and thus achieve accurate noise reduction. In this way, in a complex noise environment, the method can also achieve a good noise reduction effect. In addition, the embodiment of the present application can also set the noise that the user thinks needs to be eliminated according to demand (such as music when sleeping at night, etc.), so that the user's needs can be better met and the user's noise reduction experience can be improved.

[0027] The noise reduction system provided in the embodiment of the present application is described below.

[0028] Figure 1 is a schematic diagram of a noise reduction system provided in an embodiment of the present application. Figure 1 The noise reduction system 10 includes a collection module 101 and a noise reduction module 102 .

[0029] The noise reduction system 10 is used to collect audio signals, which may include various sound signals in the environment. The noise reduction system 10 can reduce the noise of the sound signal and output it. For example, the noise reduction system 10 can be installed in a computer device. For example, the computer device can be a tablet computer, a mobile phone, a recording device, etc., which is not limited in the embodiments of the present application.

[0030] Optionally, the noise reduction system 10 may include a display screen for setting a noise reduction mode of the noise reduction system 10 .

[0031] The acquisition module 101 is used to acquire sound signals. For example, the sound signals acquired by the acquisition module 101 may include noise signals and valid signals other than the noise signals.

[0032] Optionally, the collection module 101 may include a plurality of microphones, each of which is used to collect sound signals in various directions in the environment. For example, the microphone may be a microphone.

[0033] Optionally, the plurality of pickups in the acquisition module 101 may form a preset array. The preset array may be pre-set. For example, the preset array may be set as a linear array, a circular array, a planar array, a hybrid array, etc., which is not limited in the embodiment of the present application.

[0034] In this case, after the multiple microphones collect sound signals emitted by the same sound source, the noise reduction system 10 determines the position of the sound source according to the time when the multiple microphones collect the sound signals.

[0035] Optionally, the multiple microphones in the acquisition module 101 may include a reference microphone and an error microphone, wherein the reference microphone is used to acquire sound signals in the environment, and the error microphone is used to capture the sound signals after noise reduction.

[0036] For example, Figure 2As shown, the acquisition module 101 may include 4 microphones, namely microphone (MIC) 1, MIC2, MIC3 and MIC4. The 4 microphones are arranged in a planar array, and the 4 microphones are arranged at the upper left corner, lower left corner, upper right corner and lower right corner of the installation surface respectively. Among them, MIC1, MIC2 and MIC3 are used to collect sound signals in the environment. After MIC1, MIC2 and MIC3 collect sound signals emitted by the same sound source, the noise reduction system 10 can determine the position of the sound source according to the time difference between the sound reaching MIC1, MIC2 and MIC3, and play it after noise reduction accordingly. Among them, MIC4 is used to collect the played sound signal after noise reduction, so as to evaluate the noise reduction effect accordingly.

[0037] After the acquisition module 101 acquires the audio signal, it may send the audio signal to the noise reduction module 102 .

[0038] The noise reduction module 102 is used to reduce noise on the audio signal sent by the acquisition module 101 .

[0039] Optionally, the noise reduction module 102 may include a noise identification module, a sound preservation module, a feature extraction module, and an anti-phase sound wave generation module.

[0040] The noise recognition module is used to recognize the noise signal in the audio signal and separate the noise signal from the audio signal.

[0041] Optionally, the noise recognition module may include a deep learning algorithm (ie, the first model described below), which is used to recognize the noise signal in the audio signal and then separate the noise signal from the audio signal.

[0042] After the noise recognition module obtains the noise signal in the audio signal, the noise signal may be sent to the feature extraction module.

[0043] The sound retaining module is used to retain the audio signal after the noise signal is separated. The audio signal after the noise signal is separated can be called a useful signal (ie, the effective signal described below).

[0044] The feature extraction module may include a feature extraction model and a fusion module. The feature extraction module is used to extract the signal feature of the noise signal from the noise signal, and send the signal feature of the noise signal to the anti-phase sound wave generation module.

[0045] The anti-phase sound wave generating module is used to generate a denoising signal corresponding to the noise signal according to the signal characteristics of the noise signal.

[0046] After the anti-phase sound wave generating module generates the denoising signal corresponding to the noise signal, the noise reduction module 102 can fuse the audio signal with the denoising signals corresponding to all the noise signals in the audio signal to obtain a denoised audio signal.

[0047] Optionally, the noise reduction system 10 may further include a playing module, which is used to play the noise-reduced audio signal. For example, the playing module may be a speaker.

[0048] The noise reduction method provided in the embodiment of the present application is explained in detail below.

[0049] Figure 3 is a flow chart of a noise reduction method provided in an embodiment of the present application. The noise reduction method can be applied to the above Figure 2 The noise reduction system 10 in the embodiment, see Figure 3 , the method comprises the following steps: Step 301: The noise reduction system obtains n first audio signals, where n is an integer greater than or equal to 2.

[0050] The n first audio signals are audio signals collected by n microphones. For example, the n microphones may be the n microphones in the collection module 101 .

[0051] The first audio signal is an audio signal obtained by collecting the sound of the external environment by the microphone. The n microphones are in the same environment, and the n first audio signals are obtained by collecting the same environmental sound source by the n microphones. The environmental sound source can be a single sound source or a mixed sound source including multiple sound sources in the environment. For example, when a user is speaking, there may also be a car horn around the user, and the environmental sound source may include the user's voice and the car horn.

[0052] It should be noted that the characteristics of the audio remain basically unchanged in a short period of time, generally in a short period of time of 10 milliseconds to 30 milliseconds, that is, it is relatively stable, that is, it has short-term stability. Therefore, the processing of the audio can be based on the "short time", that is, the audio can be divided into segments of signals with smaller data volumes for processing, each of which is called a "frame" and the frame length is generally 10 milliseconds to 30 milliseconds. This not only has a fast processing speed, but also can well meet the real-time requirements. In the embodiment of the present application, the first audio signal can be the audio frame currently collected by the microphone.

[0053] After acquiring the n first audio signals, the noise reduction system may start to perform noise reduction on the n first audio signals.

[0054] Step 302: The noise reduction system determines m groups of noise signals among the n first audio signals, each group of noise signals among the m groups of noise signals includes n noise signals, and m is a positive integer.

[0055] The n noise signals in a group of noise signals are noise signals emitted by a sound source collected by the n microphones, that is, the n noise signals are noise signals with relatively similar noise characteristics.

[0056] Since each group of noise signals includes noise signals emitted by the same sound source collected by n microphones, when noise reduction is performed subsequently, each group of noise signals in the m groups of noise signals can be specifically reduced. In this way, the noise reduction effect can be significantly improved, ensuring a significant improvement in audio quality.

[0057] In some embodiments, the operation of step 302 may be: for any one of the n first audio signals, the noise reduction system inputs the first audio signal into a first model, and the first model outputs a valid signal and m noise signals in the first audio signal; and all noise signals in the n first audio signals are grouped to obtain the m groups of noise signals.

[0058] The first model is used to identify and separate the noise signal in the audio signal. For example, the first model can be a deep learning model. The first model can also be called a noise classifier.

[0059] The effective signal is an audio signal required by the user. For example, the noise reduction system can save the effective signal.

[0060] The noise signal has a sound type. The sound type of the noise signal can be preset. For example, the sound type of the noise signal can be set by default or manually set by the user. For example, the sound type of the noise signal can be an air conditioner sound, a horn sound, an engine sound, etc., which is not limited in the embodiments of the present application.

[0061] Since the sound signals emitted by the same sound source have high similarity, the n noise signals with similar time, amplitude, phase and frequency in the n first audio signals can be divided into a group of noise signals, which are emitted by the same sound source.

[0062] In the embodiment of the present application, since the first model is a pre-trained model that can identify the sound types of various sounds, during the noise reduction process of the noise reduction system, when new noise appears in the environment, the first model can accurately identify the noise and perform noise reduction accordingly.

[0063] Optionally, before the noise reduction system inputs the first audio signal into the first model, it may pre-process the first audio signal to convert the format of the first audio signal into a format suitable for input into the first model, so as to improve the accuracy of identifying the noise signal.

[0064] For ease of understanding, the following Figure 4 The above noise identification process is exemplified.

[0065] Figure 4 is a flowchart of a noise identification method provided in an embodiment of the present application, see Figure 4 The method may include the following steps 401 to 405: Step 401: The noise reduction system obtains n first audio signals, where the n first audio signals are audio signals collected by n microphones.

[0066] Step 402: The noise reduction system pre-processes the n first audio signals to obtain n processed first audio signals.

[0067] Step 403: The noise reduction system inputs the n first audio signals into the first model respectively.

[0068] Step 404: For any first audio signal among the n first audio signals, the first model performs feature extraction on the first audio signal to obtain one or more audio features.

[0069] Step 405: The first model identifies one or more noise features among the one or more audio features, separates one or more noise signals corresponding to the one or more noise features from the first audio signal, and outputs a valid signal remaining after separating the noise signal and the one or more noise signals.

[0070] In some embodiments, after the first model outputs the valid signal, the noise reduction system may also display the valid signal on a noise setting interface, where the noise setting interface includes one or more sound types, and the one or more sound types are sound types corresponding to one or more sound signals in the valid signal. The user can set the type of noise to be eliminated in the noise setting interface. This method of customizing noise types can better meet the personalized needs of users, improve user experience, and thus improve the usability of the noise reduction system.

[0071] The following describes the process of setting the noise type.

[0072] The noise type setting process may include the following steps (1) to (3): Step (1): The noise reduction system displays a noise setting interface, which includes multiple sound types.

[0073] For example, the multiple sound types may include a sound type corresponding to each sound signal in the multiple sound signals in the valid signal. And / or, the multiple sound types may include some sound types preset by a technician.

[0074] After the noise reduction system displays the noise setting interface, the user can select the sound type to be set in the noise setting interface to set the noise type.

[0075] Step (2): In response to receiving a selection operation of at least one sound type among the multiple sound types, the noise reduction system determines the at least one sound type as a noise type.

[0076] For example, the user may click on at least one sound type in the noise setting interface, and in response to the click operation, the noise reduction system may display the at least one sound type as a selected state.

[0077] Step (3): the noise reduction system adjusts the first model according to the noise type set in the noise setting interface, so that the first model is used to output a noise signal of the noise type.

[0078] It should be noted that, during the model training phase, the first model can identify and separate noise signals of multiple noise types. During the model use phase, the noise reduction system can flexibly adjust the noise types that can be separated by the first model according to actual needs (such as the user's settings in the noise setting interface), so that the noise reduction needs can be better met.

[0079] Optionally, the noise reduction system further includes one or more noise reduction modes, each of the one or more noise reduction modes corresponds to at least one sound type, and all sound types corresponding to one noise reduction mode are noise types that need to be eliminated in this noise reduction mode.

[0080] The one or more noise reduction modes may be preset. For example, the one or more noise reduction modes may be set as required. For example, the one or more noise reduction modes may include an office mode, a home mode, an outdoor mode, etc., which is not limited in the embodiments of the present application.

[0081] In this way, users can choose different noise reduction modes for noise reduction according to different scenarios, which can better meet user needs and improve the user's noise reduction experience.

[0082] The following describes the process of setting the noise reduction mode.

[0083] The noise reduction mode setting process may include the following steps (1) to (3): Step (1): The noise reduction system displays a noise reduction mode setting interface, which includes one or more noise reduction modes.

[0084] After the noise reduction system displays the noise reduction mode setting interface, the user can select a noise reduction mode for setting on the noise reduction mode setting interface.

[0085] Step (2): In response to receiving a selection operation of one of the one or more noise reduction modes, the noise reduction system determines at least one sound type corresponding to the selected noise reduction mode as a noise type.

[0086] For example, the user may click on a noise reduction mode in the noise reduction mode setting interface, and in response to the click operation, the noise reduction system may display the noise reduction mode as a selected state.

[0087] Step (3): the noise reduction system adjusts the first model according to the noise type set in the noise reduction mode setting interface, so that the first model is used to output a noise signal of the noise type.

[0088] It should be noted that, during the model training phase, the first model can identify and separate noise signals of multiple noise types. During the model use phase, the noise reduction system can flexibly adjust the noise types that can be separated by the first model according to actual needs (such as the user's settings in the noise reduction mode setting interface), so that the noise reduction needs can be better met.

[0089] Step 303: The noise reduction system determines m sound source positions according to the m groups of noise signals, and the m sound source positions correspond one-to-one to the m groups of noise signals.

[0090] For example, for any one of the m groups of noise signals, the noise reduction system can determine the time delay difference (TDOA) of signal acquisition according to the moment when the n microphones collect the n noise signals in the group of noise signals, and then determine the direction of arrival (DOA) of the sound source corresponding to the group of noise signals relative to the n microphones according to the time delay difference, and the time delay difference and the direction of arrival can represent the position of the sound source corresponding to the group of noise signals. For example, the noise reduction system can determine the time delay difference by a generalized cross-correlation phase transform (GCC-PHAT) algorithm.

[0091] For example, the noise reduction system can determine the direction of arrival according to the delay difference by using the following formula: ; in, For the wave direction; is the speed of sound (about 340 m / s); is the delay difference; is the spacing between pickups.

[0092] After determining the sound source position corresponding to each group of noise signals in the m groups of noise signals, the noise reduction system can subsequently generate a more accurate denoising signal based on the sound source position corresponding to the noise signal. Moreover, when the sound source position corresponding to the noise signal changes, the noise reduction system can determine the changed sound source position in real time, thereby ensuring the stability and accuracy of noise reduction.

[0093] Step 304: For any group of noise signals among the m groups of noise signals, the noise reduction system determines the signal characteristics of the group of noise signals according to the group of noise signals and the sound source positions corresponding to the group of noise signals, and generates a denoised signal corresponding to the group of noise signals according to the signal characteristics of the group of noise signals.

[0094] For example, the noise reduction system can generate a denoised signal corresponding to the set of noise signals through an inverse sound wave generation module. For example, the noise reduction system can restore the signal characteristics of the set of noise signals to a spectrum diagram, and then convert the spectrum diagram into a time domain signal opposite to the noise signal (such as through an inverse short-time Fourier transform conversion), and finally normalize the amplitude of the time domain signal to obtain a denoised signal corresponding to the set of noise signals.

[0095] Since sound is affected by distance, direction and environmental factors during propagation, and the location of the sound source can reflect important information such as the sound propagation path and environment, the signal characteristics of the noise signal can be determined more accurately based on the noise signal and the sound source location corresponding to the noise signal. After the denoising signal is subsequently generated based on the signal characteristics, the denoising signal can effectively eliminate the noise signal without affecting other sound signals, thereby improving the noise reduction effect.

[0096] It should be noted that in the embodiment of the present application, when generating a denoising signal, the inverted sound wave generation module can automatically adjust parameters according to the current environment, such as adjusting the frequency filtering range, enhancing the noise suppression strength of a specific frequency band, etc., to ensure that a good noise reduction effect can be achieved in different noise change scenarios. For example, in a noisy urban environment, the low-frequency engine sound can be suppressed, while in a quieter indoor environment, the processing of high-frequency noise can be reduced to keep the audio natural and clear after noise reduction.

[0097] In some embodiments, the operation of the noise reduction system determining the signal characteristics of the set of noise signals according to the set of noise signals and the sound source positions corresponding to the set of noise signals may include the following steps (1) to (2): Step (1): For any one noise signal in the group of noise signals, the noise reduction system preprocesses the noise signal and the sound source positions corresponding to the group of noise signals to obtain a group of input data corresponding to the noise signal.

[0098] Preprocessing the noise signal and the sound source positions corresponding to the group of noise signals refers to processing the noise signal and the sound source positions corresponding to the group of noise signals into input data suitable for the second model.

[0099] By preprocessing a group of noise signals and their corresponding sound source positions, the format of the group of noise signals and their corresponding sound source positions can be unified and standardized to meet the input standard of the second model, thereby ensuring the accuracy of the signal characteristics output by the second model.

[0100] For example, the noise reduction system can segment the noise signal to obtain multiple audio segments, then extract the audio features of each of the multiple audio segments, and then respectively splice the audio features of each of the multiple audio segments with the position features of the sound source position corresponding to the group of noise signals to obtain multiple target features, which are a group of input data corresponding to the noise signal.

[0101] It should be noted that, since the noise signal is continuous in time, the multiple audio segments obtained by segmenting it have a temporal order, forming a time series. Each audio segment in the multiple audio segments is spliced ​​with the position feature of the sound source position corresponding to the noise signal, and the multiple target features obtained also maintain a temporal order, forming a time series.

[0102] After the noise reduction system preprocesses the group of noise signals and the sound source positions corresponding to the group of noise signals to obtain n groups of input data corresponding to the group of noise signals, the n groups of input data can be input into the second model to obtain the signal characteristics of the group of noise signals.

[0103] Step (2): The noise reduction system inputs n groups of input data corresponding to the group of noise signals into the second model, and the second model outputs signal features of the group of noise signals.

[0104] For example, the noise reduction system inputs the n groups of input data into the n channels in the second model one by one. The second model can perform feature extraction on the input data of each of the n channels to obtain m signal features, and then fuse the n signal features across channels to obtain the signal features of the group of noise signals.

[0105] Optionally, the second model includes a feature extraction model and a fusion module, the feature extraction model includes a state space model (SSM) and a transformer model. Optionally, both the SSM and the transformer model may have n channels.

[0106] The SSM is used to output a first signal feature according to the n sets of input data, the transformer model is used to output a second signal feature according to the n sets of input data, and the fusion module is used to output a target signal feature according to the first signal feature and the second signal feature. The target signal feature is the signal feature of the set of noise signals.

[0107] For example, after the denoising system inputs the n sets of input data into the second model, the SSM in the second model can perform feature extraction on each of the n sets of input data to obtain n signal features, then perform feature fusion on the n signal features to obtain first signal features, and send the first signal features to the fusion module; the transformer model in the second model can perform feature extraction on each of the n sets of input data to obtain n signal features, then perform feature fusion on the n signal features to obtain second signal features, and send the second signal features to the fusion module. The fusion module can perform feature fusion on the first signal features and the second signal features to obtain target signal features.

[0108] For example, the SSM can determine the signal characteristics by the following formula: ; ; in, represents the state vector of SSM at time t; is the state transfer matrix; represents the state vector of SSM at time t-1; is the input matrix; For input data; is the signal feature; is the observation matrix; is the observation input matrix.

[0109] For example, the transformer model can determine the signal characteristics through the following formula: ; in, is the signal feature; is the query vector; is the key vector; is a value vector; is the dimension scaling factor; To perform the transpose operation on the K vector.

[0110] The SSM in the second model can efficiently capture the short-term dependencies of stationary signals, which enables it to quickly adapt to changes in signals when processing mutation signals. When noise mutations occur, SSM can accurately generate signal features of the input data. And the computational complexity of SSM is linear, which makes it more efficient when processing large-scale input data. The transformer model in the second model can capture long-distance dependencies through the self-attention mechanism, so it performs well when processing audio signals with global context information. This enables the transformer model to extract signal features more accurately when the noise is relatively stable. In this way, whether the noise is stable or the noise mutation occurs, the second model can output more accurate signal features, which can significantly improve the noise reduction effect.

[0111] In some embodiments, the fusion module can concatenate the first signal feature and the second signal feature to obtain a third signal feature; input the third signal feature into a gating model, and the gating model outputs a first weight; based on the first weight and the second weight, perform weighted fusion on the first signal feature and the second signal feature to obtain a target signal feature, where the second weight is 1 minus the first weight.

[0112] This gating model can dynamically adjust the weights according to the changes in the noise signal, so that the fused target signal features can better adapt to the scenarios of noise mutation and stable noise.

[0113] The first weight is the weight corresponding to the SSM. The second weight is the weight corresponding to the transformer model.

[0114] The gating model can adaptively adjust the weights of the signal features output by the SSM and transformer models. In this way, in the case of a sudden noise change, the weight of the first signal feature can be made higher so that the target signal feature can highlight the signal feature when the noise changes suddenly; in the case of stable noise, the weight of the second signal feature can be made higher so that the target signal feature can highlight the signal feature when the noise is stable. By dynamically adjusting the weights of the signal features, the fusion module can better handle the situations of sudden noise changes and stable noise, thus improving the accuracy of the output target signal features and the noise reduction effect.

[0115] Step 305: The noise reduction system fuses the n first audio signals with the denoised signal corresponding to each noise signal in the m groups of noise signals to obtain a second audio signal.

[0116] The second audio signal is an audio signal obtained after noise reduction is performed on the n first audio signals.

[0117] Since the m denoising signals can eliminate the m groups of noise signals, the n first audio signals and the denoising signal corresponding to each noise signal in the m groups of noise signals can be fused to obtain a noise-reduced audio signal (ie, a second audio signal).

[0118] For example, the noise reduction system may combine the n first audio signals to obtain a fourth audio signal, and then based on a time alignment principle, superimpose the fourth audio signal and the m denoised signals to obtain a second audio signal.

[0119] Optionally, after obtaining the second audio signal, the noise reduction system may play the second audio signal; alternatively, the noise reduction system may save the second audio signal for recording; alternatively, the noise reduction system may also send the second audio signal to other devices.

[0120] In some embodiments, after obtaining the second audio signal, the noise reduction system can fuse all valid signals in the n first audio signals to obtain a third audio signal; determine a signal residual between the second audio signal and the third audio signal; and update parameters in the gating model and the feature extraction model according to the signal residual.

[0121] The third audio signal is an audio signal after noise reduction in an ideal state.

[0122] The signal residual is used to evaluate the noise reduction effect. Specifically, the smaller the signal residual is, the smaller the difference between the third audio signal and the second audio signal is, that is, the better the noise reduction effect is; the larger the signal residual is, the larger the difference between the third audio signal and the second audio signal is, that is, the worse the noise reduction effect is. Therefore, the parameters in the gating model and the feature extraction model can be updated according to the signal residual to optimize the noise reduction capability of the noise reduction system.

[0123] In this way, when a new noise source appears in the environment, or when the existing noise suddenly changes, the noise reduction parameters of the noise reduction system can be continuously adjusted to adapt the noise reduction system to the current environment, so that the signal characteristics of the noise signal in the environment can be more accurately identified, and the noise reduction ability of the noise reduction system can be guaranteed. For example, when there is a sudden increase in traffic noise or the start of some mechanical equipment, the noise reduction system can quickly identify this change and adjust the noise reduction parameters accordingly. In this way, the noise reduction ability of the noise reduction system can quickly adapt to the current environment, so that the noise signal characteristics in the current environment can be accurately determined.

[0124] For example, the noise reduction system may determine the signal residual according to the second audio signal and the third audio signal by using the following formula: ; in, is the signal residual; is a second audio signal; is the third audio signal.

[0125] For example, the noise reduction system can determine a loss value based on the signal residual through a loss function (such as a mean square error function), and then use a back propagation algorithm to update the parameters in the gating model and the feature extraction model respectively based on the loss value.

[0126] For ease of understanding, the following Figure 5 The above noise elimination process is exemplarily described.

[0127] Figure 5 It is a schematic diagram of a noise elimination process provided in an embodiment of the present application.

[0128] like Figure 5 As shown, the noise reduction system inputs multiple groups of input data into a feature extraction model, and the feature extraction model extracts features from the multiple groups of input data and outputs a first signal feature and a second signal feature; the fusion module fuses the first signal feature and the second signal feature to obtain a target signal feature. The anti-phase sound wave generation module generates a denoised signal according to the target signal feature. The denoised signal fusion module fuses the first audio signal with the denoised signal to obtain a second audio signal. The feedback controller determines a signal residual according to the second audio signal, updates the parameters in the fusion module according to the signal residual, and updates the parameters in the feature extraction model according to the signal residual.

[0129] In an embodiment of the present application, after the noise reduction system obtains n first audio signals collected by n microphones, m groups of noise signals in the n first audio signals are determined, and each group of noise signals in the m groups of noise signals includes n noise signals. Then, m sound source positions corresponding to the m groups of noise signals are determined. For any group of noise signals in the m groups of noise signals, the signal characteristics of the group of noise signals are determined according to the group of noise signals and their corresponding sound source positions, and corresponding denoising signals are generated according to the signal characteristics of the group of noise signals. Afterwards, the denoising signals corresponding to the n first audio signals and each group of noise signals in the m groups of noise signals are fused to obtain a second audio signal. In an embodiment of the present application, the noise signal characteristics are determined according to the noise signal and its corresponding sound source position. The noise signal characteristics can more accurately reflect the characteristics of the noise signal in space. In this way, the denoising signal generated according to the noise signal characteristics can effectively eliminate the noise signal, thereby improving the noise reduction effect.

[0130] Figure 6 is a schematic diagram of a noise reduction device provided in an embodiment of the present application. The device can be implemented as part or all of a computer device by software, hardware, or a combination of both. The computer device can be as follows Figure 7 Computer equipment shown. Figure 6 The device includes: an acquisition module 601, a first determination module 602, a second determination module 603, a third determination module 604, and a first fusion module 605.

[0131] An acquisition module 601 is used to acquire n first audio signals, where the n first audio signals are audio signals collected by n microphones, and n is an integer greater than or equal to 2; A first determination module 602 is configured to determine m groups of noise signals among the n first audio signals, each group of noise signals among the m groups of noise signals includes n noise signals, where m is a positive integer; A second determination module 603 is used to determine m sound source positions according to the m groups of noise signals, where the m sound source positions correspond one-to-one to the m groups of noise signals; A third determination module 604 is configured to determine, for any one of the m groups of noise signals, signal characteristics of the group of noise signals according to the group of noise signals and sound source positions corresponding to the group of noise signals, and generate a denoised signal corresponding to the group of noise signals according to the signal characteristics of the group of noise signals; The first fusion module 605 is configured to fuse the n first audio signals with a denoised signal corresponding to each noise signal in the m groups of noise signals to obtain a second audio signal.

[0132] Optionally, the first determining module 602 is used to: For any one of the n first audio signals, input the first audio signal into a first model, and the first model outputs a valid signal in the first audio signal and m noise signals; All noise signals in the n first audio signals are grouped to obtain the m groups of noise signals.

[0133] Optionally, the third determining module 604 is used to: For any one noise signal in the group of noise signals, preprocessing the noise signal and the sound source position corresponding to the group of noise signals is performed to obtain a group of input data corresponding to the noise signal; The n groups of input data corresponding to the group of noise signals are input into the second model, and the second model outputs the signal features of the group of noise signals.

[0134] Optionally, the third determining module 604 is used to: The noise signal is segmented to obtain a plurality of audio segments; Extracting audio features of each audio segment from the multiple audio segments; The audio features of each audio segment in the multiple audio segments are respectively spliced ​​with the position features of the sound source positions corresponding to the group of noise signals to obtain multiple target features, where the multiple target features are a group of input data corresponding to the noise signal.

[0135] Optionally, the second model includes a feature extraction model and a fusion module, and the feature extraction model includes a state space model SSM and a transformer model; The SSM is used to output a first signal feature based on the n groups of input data, the transformer model is used to output a second signal feature based on the n groups of input data, and the fusion module is used to output a target signal feature based on the first signal feature and the second signal feature, and the target signal feature is the signal feature of the group of noise signals.

[0136] Optionally, the fusion module is used to: concatenate the first signal feature and the second signal feature to obtain a third signal feature; input the third signal feature into a gating model, and the gating model outputs a first weight, where the first weight is the weight corresponding to the SSM; based on the first weight and the second weight, perform weighted fusion on the first signal feature and the second signal feature to obtain the target signal feature, where the second weight is 1 minus the first weight, and the second weight is the weight corresponding to the transformer model.

[0137] Optionally, the device further comprises: A second fusion module, used for fusing all valid signals in the n first audio signals to obtain a third audio signal; a fourth determining module, configured to determine a signal residual between the second audio signal and the third audio signal; An updating module is used to update parameters in the gating model and the feature extraction model according to the signal residual.

[0138] Optionally, the device further comprises: A first display module, used to display a noise setting interface, the noise setting interface including multiple sound types; a fifth determination module, configured to, in response to receiving a selection operation on at least one sound type among the multiple sound types, determine the at least one sound type as a noise type; The first adjustment module is used to adjust the first model according to the noise type set in the noise setting interface, so that the first model is used to output a noise signal of the noise type.

[0139] Optionally, the device further comprises: A second display module, used to display a noise reduction mode setting interface, where the noise reduction mode setting interface includes one or more noise reduction modes; a sixth determining module, configured to, in response to receiving a selection operation of one of the one or more noise reduction modes, determine at least one sound type corresponding to the selected noise reduction mode as a noise type; The second adjustment module is used to adjust the first model according to the noise type set in the noise reduction mode setting interface, so that the first model is used to output a noise signal of the noise type.

[0140] In an embodiment of the present application, after the noise reduction system obtains n first audio signals collected by n microphones, m groups of noise signals in the n first audio signals are determined, and each group of noise signals in the m groups of noise signals includes n noise signals. Then, m sound source positions corresponding to the m groups of noise signals are determined. For any group of noise signals in the m groups of noise signals, the signal characteristics of the group of noise signals are determined according to the group of noise signals and their corresponding sound source positions, and corresponding denoising signals are generated according to the signal characteristics of the group of noise signals. Afterwards, the denoising signals corresponding to the n first audio signals and each group of noise signals in the m groups of noise signals are fused to obtain a second audio signal. In an embodiment of the present application, the noise signal characteristics are determined according to the noise signal and its corresponding sound source position. The noise signal characteristics can more accurately reflect the characteristics of the noise signal in space. In this way, the denoising signal generated according to the noise signal characteristics can effectively eliminate the noise signal, thereby improving the noise reduction effect.

[0141] It should be noted that: the noise reduction device provided in the above embodiment only uses the division of the above-mentioned functional modules as an example for explanation when performing noise reduction. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0142] The functional modules in the above embodiments may be integrated into one processing unit, or each functional module may exist physically as a processing unit alone, or two or more functional modules may be integrated into one processing unit, and the above processing unit may be implemented in the form of hardware or software. In addition, the specific names of the functional modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present application.

[0143] The noise reduction device and the noise reduction method provided in the above embodiments belong to the same concept. The specific working process of the functional modules in the above embodiments and the technical effects brought about can be found in the method embodiment part, which will not be repeated here.

[0144] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 7 As shown, the computer device 7 includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70. When the processor 70 executes the computer program 72, the steps of the noise reduction method in the above embodiment are implemented.

[0145] The computer device 7 may be a general-purpose computer device or a dedicated computer device. In a specific implementation, the computer device 7 may be a desktop computer, a portable computer, a network server, a PDA, a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of the computer device 7. Those skilled in the art will understand that Figure 7 It is only an example of the computer device 7 and does not constitute a limitation on the computer device 7. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, etc.

[0146] The processor 70 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0147] In some embodiments, the memory 71 may be an internal storage unit of the computer device 7, such as a hard disk or memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the computer device 7. The memory 71 is used to store an operating system, an application program, a boot loader, data, and other programs. The memory 71 may also be used to temporarily store data that has been output or is to be output.

[0148] An embodiment of the present application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, and when the processor executes the computer program, the steps in any of the above-mentioned method embodiments are implemented.

[0149] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0150] An embodiment of the present application provides a computer program product, which, when executed on a computer, enables the computer to execute the steps in the above-mentioned method embodiments.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above method embodiments, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, ROM (read-only memory), RAM (random access memory), CD-ROM (compact disc read-only memory), magnetic tape, floppy disk and optical data storage device. The computer-readable storage medium mentioned in the present application can be a non-volatile storage medium, in other words, it can be a non-transient storage medium.

[0152] It should be understood that all or part of the steps to implement the above embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions can be stored in the above-mentioned computer readable storage medium.

[0153] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0154] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0155] In the embodiments provided in the present application, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0156] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0157] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant regulations and standards of relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0158] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A noise reduction method, characterized in that: The method comprises: Acquire n first audio signals, where the n first audio signals are audio signals collected by n microphones, and n is an integer greater than or equal to 2; Determine m groups of noise signals among the n first audio signals, each group of noise signals among the m groups of noise signals includes n noise signals, and m is a positive integer; Determine m sound source positions according to the m groups of noise signals, wherein the m sound source positions correspond one-to-one to the m groups of noise signals; For any one group of noise signals among the m groups of noise signals, determine signal characteristics of the group of noise signals according to the group of noise signals and sound source positions corresponding to the group of noise signals, and generate denoised signals corresponding to the group of noise signals according to the signal characteristics of the group of noise signals; The n first audio signals are fused with a denoised signal corresponding to each noise signal in the m groups of noise signals to obtain a second audio signal.

2. The method according to claim 1, characterized in that The determining m groups of noise signals among the n first audio signals comprises: For any one of the n first audio signals, input the first audio signal into a first model, and the first model outputs a valid signal and m noise signals in the first audio signal; All noise signals in the n first audio signals are grouped to obtain the m groups of noise signals.

3. The method according to claim 1 or 2, characterized in that The step of determining the signal characteristics of the group of noise signals according to the group of noise signals and the sound source positions corresponding to the group of noise signals comprises: For any one noise signal in the group of noise signals, preprocessing the noise signal and the sound source positions corresponding to the group of noise signals is performed to obtain a group of input data corresponding to the noise signal; n groups of input data corresponding to the group of noise signals are input into a second model, and the second model outputs signal features of the group of noise signals.

4. The method according to claim 3, characterized in that The preprocessing of the noise signal and the sound source positions corresponding to the group of noise signals to obtain a group of input data corresponding to the noise signal includes: Segmenting the noise signal to obtain multiple audio segments; Extracting audio features of each audio segment in the plurality of audio segments; The audio features of each audio segment in the multiple audio segments are respectively concatenated with the position features of the sound source positions corresponding to the group of noise signals to obtain multiple target features, where the multiple target features are a group of input data corresponding to the noise signals.

5. The method according to claim 3, characterized in that The second model includes a feature extraction model and a fusion module, and the feature extraction model includes a state space model SSM and a transformer model; The SSM is used to output a first signal feature based on the n groups of input data, the transformer model is used to output a second signal feature based on the n groups of input data, and the fusion module is used to output a target signal feature based on the first signal feature and the second signal feature, wherein the target signal feature is the signal feature of the group of noise signals.

6. The method according to claim 5, characterized in that The fusion module is used to: concatenate the first signal feature and the second signal feature to obtain a third signal feature; input the third signal feature into a gating model, and the gating model outputs a first weight, where the first weight is a weight corresponding to the SSM; Based on the first weight and the second weight, the first signal feature and the second signal feature are weightedly fused to obtain the target signal feature, where the second weight is obtained by subtracting the first weight from 1, and the second weight is the weight corresponding to the transformer model.

7. The method according to claim 6, characterized in that After fusing the n first audio signals with the denoised signal corresponding to each noise signal in the m groups of noise signals to obtain the second audio signal, the method further includes: Merging all valid signals of the n first audio signals to obtain a third audio signal; determining a signal residual between the second audio signal and the third audio signal; Parameters in the gating model and the feature extraction model are updated according to the signal residual.

8. The method according to claim 2, characterized in that The method further comprises: Display a noise setting interface, wherein the noise setting interface includes multiple sound types; In response to receiving a selection operation of at least one sound type among the plurality of sound types, determining the at least one sound type as a noise type; According to the noise type set in the noise setting interface, the first model is adjusted so that the first model is used to output a noise signal of the noise type.

9. The method according to claim 2, characterized in that The method further comprises: Displaying a noise reduction mode setting interface, wherein the noise reduction mode setting interface includes one or more noise reduction modes; In response to receiving a selection operation of one of the one or more noise reduction modes, determining at least one sound type corresponding to the selected noise reduction mode as a noise type; According to the noise type set in the noise reduction mode setting interface, the first model is adjusted so that the first model is used to output a noise signal of the noise type.

10. A computer device, characterized in that: The computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 9 when executed by the processor.

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