72 results about "Speech denoising" patented technology

The invention relates to an array microphone noise reduction recording method based on cascade noise reduction and blind source separation, which belongs to the technical field of voice signal processing and recording, and comprises the following steps: configuring a multi-channel array microphone, ensuring that the amplitude and phase of a channel signal are consistent, and collecting an original multi-channel voice signal; a weighted kernel function blind source separation algorithm is adopted, signal-to-noise ratio distribution characteristics of signals are extracted, kernel function weights are given, and target voice and interference signal components are obtained through decoupling of an independent component analysis model; executing target-oriented adaptive cascade noise reduction, locking the voice of a keynote speaker through directional pickup, reducing noise, filtering out reverberation, and enhancing the voice of a far-field target by combining a voice mask neural network with a far-field pickup algorithm in sequence; and processing the target voice through voice feature perception lossless coding and storing the target voice. According to the invention, stable acquisition of multi-channel signals, accurate separation of mixed signals and layered suppression of noise reverberation are realized, the signal-to-noise ratio and definition of far-field voice are significantly improved, and the method is suitable for single-person speaking or multi-person dialogue scenes.

The invention particularly relates to a voice noise reduction method suitable for different noise environments, and relates to the technical field of voice signal processing. Analyzing a noise environment and characteristics; adaptive selection of a noise reduction algorithm; adaptive noise reduction processing is executed; and noise reduction effect evaluation and iterative optimization are carried out. According to the invention, refined noise classification is matched with the algorithm, so that the limitation of a traditional single algorithm in a complex environment is solved; the steady-state noise is dynamically counteracted by adopting an adaptive filter, the frequency domain gain of the unsteady-state noise is adjusted in real time through Wiener filtering, and the pulse noise is subjected to dynamic threshold processing by combining median filtering and wavelet transform; especially, the design of cooperation factors in wavelet transform can accurately distinguish signal details and noise: intensively suppress pulse noise, loosely retain details such as voices and consonants, and realize dynamic balance of noise reduction intensity and signal distortion.

The invention discloses a voice noise reduction method and device for a multi-person scene, electronic equipment and a storage medium, and relates to the technical field of voice signal processing. The method comprises the following steps: acquiring an audio signal and a video image of a target space scene; determining audio perception information matched with the audio signal and a visual voice activity detection result corresponding to the video image; fusing the audio perception information and the visual voice activity detection result, and determining a target voice sounding object when the voice activity fusion result indicates that the voice activity exists; and positioning a human face corresponding to the target voice sounding object to obtain position change information, updating a pickup direction, controlling beam forming processing, and outputting a denoised target voice signal. According to the scheme provided by the invention, the voice of the current effective speaker can be accurately separated and enhanced from the aliasing audio signals, meanwhile, the interference of other speakers and environmental noise is inhibited, and help is provided for realizing high-quality voice interaction and recognition.

The invention relates to the technical field of intelligent communication, in particular to an AI (Artificial Intelligence)-based communication voice noise reduction method, which comprises the following steps of: 1, collecting noise of multiple scenes, recording noisy voice through a microphone array, and covering complex environments such as traffic, restaurants, meetings and the like; 2, constructing a dialect voice database, adding a noise mixing layer (such as sudden whistling and keyboard tapping) and a scene evolution layer, and simulating a real environment; according to the method, multi-scene noise is collected, noisy voice is recorded through a microphone array, a dialect voice database is constructed, a noise mixing layer is added, voice features of different dialects in a complex environment are simulated, a deep learning model is deployed on edge equipment, and low-delay noise reduction is achieved through real-time processing. Mechanical noise, aerodynamic noise and external environment noise are distinguished through the dynamic noise recognition module, the noise reduction intensity is adjusted to adapt to scenes such as elevators, meeting rooms and vehicles, and therefore the method can adjust the noise reduction intensity in a self-adaptive mode.

The invention discloses a noise suppression method and system for a microphone, and belongs to the technical field of audio signal processing. The method comprises the following steps: main audio processing equipment acquires an original audio stream to obtain complex frequency spectrum data of a current frame; synchronously acquiring real-time operation state data of the audio processing equipment; dynamically generating a task allocation strategy; generating a subtask processing result, and performing data exchange; constructing a complete full-band gain matrix for the current frame; and reconstructing a denoised time domain audio signal through inverse transformation. According to the invention, the noise reduction task is intelligently decomposed and dynamically allocated to the heterogeneous computing units of the main and auxiliary devices for cooperative processing through a cooperative scheduling mechanism, so that the technical effect of realizing high-quality and low-delay real-time voice noise reduction on the mobile terminal with limited resources is achieved; the problems of processing delay, asynchronous state updating, reduced noise reduction quality and incoherent voice caused by insufficient computing power of the terminal in the prior art are solved.

The invention discloses a voice noise reduction method and system based on subspace filtering, a medium and equipment, and belongs to the technical field of software engineering and communication, and the method comprises the steps: carrying out the framing processing of noisy voice; extracting an autocorrelation coefficient and a Mel-frequency cepstral coefficient of each frame, splicing the autocorrelation coefficient and the Mel-frequency cepstral coefficient to form a feature vector, inputting the feature vector into a noise estimation network, and predicting to obtain a noise autocorrelation coefficient of each frame; according to the autocorrelation coefficient of each frame, the noise autocorrelation coefficient and the unit matrix, constructing a subspace separation matrix and carrying out eigenvalue decomposition to obtain eigenvalues and a eigenmatrix; converting the framing data into a subspace by using the feature matrix to obtain a subspace signal in each direction; calculating a subspace gain matrix in combination with the characteristic value, and filtering the subspace signal to obtain a noise reduction signal; and performing inverse transformation on the noise reduction signal according to the feature matrix to obtain noise reduction voice frames, and performing frame combination to obtain noise reduction voice. Therefore, by implementing the invention, the voice noise reduction effect can be improved, and the calculation amount and complexity of the algorithm can be reduced.

The application provides a speech noise reduction network training method and device, electronic equipment and computer readable storage medium, comprising: performing short-time Fourier transform on sample speech data in a sample data set to obtain sample time-frequency domain features; the sample speech data includes noise speech data and clean speech data, and the sample time-frequency domain features include noise time-frequency domain features and clean time-frequency domain features; calculating the noise time-frequency domain features through a neural network model to obtain predicted time-frequency domain features; evaluating the difference between the predicted time-frequency domain features and the clean time-frequency domain features through a loss function to obtain a function value, and determining whether the function value is less than a preset loss threshold; wherein the loss function is switched periodically during the training process; if yes, it is determined that the neural network model converges, and a speech noise reduction network is obtained. According to the application, the speech noise reduction network considering noise reduction and speech fidelity effect is trained without increasing model parameters.

The application provides an interactive method with voice noise reduction, a computer device, a storage medium and a system. The interactive method with voice noise reduction comprises the following steps: obtaining a user voice signal picked up by a voice collector; performing voice noise reduction on the user voice signal; uploading the user voice signal after voice noise reduction to a voice recognition cloud; obtaining semantic information obtained after processing by the voice recognition cloud; and generating a task code based on the semantic information, the task code being used to be transmitted to an executing mechanism of an indoor robot to solve the problem that the robot can accurately locate the position of a user in a complex indoor environment.

The application discloses a voice noise reduction method and device based on a memory-computing integrated chip, a medium and equipment. The method comprises the following steps: extracting weight data corresponding to a preset level of a voice noise reduction model to obtain a first weight matrix; performing positive and negative weight division processing on the first weight matrix to obtain a second weight matrix; performing weight quantization on the second weight matrix to obtain a third weight matrix; adding an additional weight to the third weight matrix to obtain a target weight matrix; and adjusting the conductance value of a memristor in a pre-constructed memory-computing integrated chip according to the elements of the target weight matrix to embed the voice noise reduction model in the memory-computing integrated chip to obtain a voice noise reduction chip. The voice noise reduction model is integrated on the memory-computing integrated chip to reduce noise, the difficulty of deploying the voice noise reduction model on the terminal side is reduced, and the efficiency of voice noise reduction is improved.

The invention discloses a voice noise reduction method and device, an earphone, a medium and a program product. The method comprises the following steps: acquiring voice sequence data; performing at least one self-attention operation on the voice sequence data; obtaining the output of the self-attention mechanism; noise reduction is carried out according to the output of the self-attention mechanism; the sg attention operation comprises the following steps: carrying out linear processing on the input sequence data by utilizing a preset query-key matrix and a preset value matrix to obtain a query-key vector and a value vector; the query-key matrix corresponds to a product between a preset query matrix and a preset key matrix; obtaining a target attention score according to the transpose of the voice sequence data and the query-key vector; carrying out Softmax operation on the target attention score to obtain an attention weight; and performing matrix multiplication on the attention weight and the value vector to obtain the output of the self-attention operation. In this way, the harsh requirement of the embedded device for high real-time performance in a voice interaction scene can be met.

The invention relates to the technical field of sailor assessment management, and particularly discloses an online management method and system based on sailor assessment, and the method comprises the steps: collecting an original voice signal during sailor assessment, and a propeller rotating speed, a sea condition grade and a ship navigational speed at a corresponding moment; the original voice signals and the physical parameters are input into a physical constraint noise reduction network to serve as a regularization item embedding loss function, ocean background noise is separated, and pure sailor voice signals are output; inputting the pure sailor voice signals into a navigation term recognition network, calculating the similarity between voice features and IMO standard term prototype vectors, and outputting term accuracy scores and term confusion point information; based on the pure crew voice signal, the term accuracy score and the sea condition level, a voice signal-to-noise ratio, term accuracy and a sea condition influence factor are fused to calculate a communication capability index; generating a personalized performance evaluation report and pushing training courses; according to the invention, the problems of difficult voice noise reduction and disjunction between evaluation and training in a complex marine environment are solved.

The invention particularly relates to a voice noise reduction method based on deep spanning feature extraction and feature cross fusion, which comprises the following steps: a double-flow network structure comprising an amplitude feature extraction branch and a phase feature extraction branch is constructed, the amplitude feature extraction branch adopts a multi-scale coding-decoding structure and a time sequence convolutional network for joint modeling, and the phase feature extraction branch adopts a multi-scale coding-decoding structure; local amplitude details are extracted to be dependent on long-range time; the phase feature extraction branch uses a hierarchical decoding structure for recovering multi-level phase structure features. Moreover, a cross-flow feature fusion mechanism is designed between double flows, so that the amplitude features and the phase features are dynamically complementary in the deep network. According to the method, acoustic physical priori and data driving characteristics are combined, noise reduction can be accurately carried out under complex background noise, and the performance of a voice noise reduction task in a non-stationary noise environment is effectively improved; therefore, problems of insufficient time-frequency correlation modeling, insufficient amplitude and phase feature utilization and the like of the existing speech enhancement technology in a complex noise scene can be solved.

The application discloses a cockpit voice noise reduction function automatic test method, device and equipment, and relates to the technical field of noise reduction test. The method comprises the following steps: collecting mixed audio data flow of a target vehicle cockpit, analyzing the mixed audio data flow to obtain noise audio data; performing noise reduction processing on the noise audio data to obtain noise reduction audio data, and converting the noise reduction audio data into test audio data in a target playback format; determining a frequency band noise reduction comprehensive score and a defect distribution atlas of the test audio data based on the difference values of the test audio data and reference audio data on different target frequency bands; and generating a test result of the voice noise reduction function based on the frequency band noise reduction comprehensive score and the defect distribution atlas of the test audio data. In the foregoing manner, discrete test steps are integrated into a continuous automatic flow, differences caused by human operation are eliminated as much as possible, distortion in the transmission or conversion process is basically avoided, and the accuracy of the test result is improved.

The application provides a multi-modal fusion voice noise reduction method and device, electronic equipment and storage medium, comprising: collecting multi-modal signals and aligning the multi-modal signals to obtain synchronous data streams, wherein the multi-modal signals include AC microphone signals, BC microphone signals and IMU signals; performing air conduction difference noise reduction on two AC microphone signals to obtain AC difference synchronous signals; determining whether it is valid speech through a preset voice activity detection mechanism; in response to valid speech, performing fusion processing on the AC difference synchronous signals, BC synchronous signals and IMU synchronous signals based on a constructed neural network to generate a pure speech signal. Through the collection and fusion processing of multi-modal data, the application has stronger robustness than the traditional single-modal or AC+BC noise reduction method, can reduce non-speech interference, and is suitable for extreme noise environments with low signal-to-noise ratio.

The invention provides a multi-modal fusion voice noise reduction method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-modal signals, and carrying out the data alignment of the multi-modal signals, so as to obtain a synchronous data stream, the multi-modal signals comprising an AC microphone signal, a BC microphone signal and an IMU signal; the method comprises the following steps: performing air conduction differential noise reduction on two paths of AC microphone signals to obtain an AC differential synchronization signal; judging whether the voice is effective or not through a preset voice activity detection mechanism; and in response to the effective voice, performing fusion processing on the AC differential synchronization signal, the BC synchronization signal and the IMU synchronization signal based on the constructed neural network to generate a pure voice signal. Compared with a traditional single-mode or AC + BC noise reduction method, the method has the advantages that through collection and fusion processing of the multi-mode data, the robustness is higher, non-voice interference can be reduced, and the method is suitable for an extreme noise environment with a low signal-to-noise ratio.

The invention belongs to the technical field of artificial intelligence, and relates to a voice noise reduction and voice recognition joint optimization method under a multi-task learning framework, which comprises the following steps: acquiring an original voice signal flow containing environmental noise; integrating the analysis results of the two channels to generate joint context intelligence; generating a multi-dimensional processing strategy instruction set comprising a plurality of control dimensions for each time-frequency unit according to the joint context intelligence; driving the parameterization noise reduction processor to process the initial time-frequency domain characteristics so as to output customized noise-reduced characteristics; constructing an initial recognition hypothesis path containing a plurality of candidate recognition results and corresponding confidence coefficients based on the joint context information; and selecting the candidate recognition result with the highest confidence coefficient after re-scoring, and decoding the candidate recognition result into a final recognition text. The method solves the problems that the overall performance of the system is limited due to lack of information interaction and cooperation, a globally optimal solution cannot be formed, and variable and complex real noise scenes are difficult to deal with.

The invention discloses a real-time voice noise reduction method and system based on a deep neural network, and is applied to the technical field of voice noise reduction. The method comprises the following steps: obtaining a pure voice sample, and adding random noise to the pure voice sample to obtain a noisy voice sample; preprocessing the voice sample to obtain a training data set; constructing a generative adversarial neural network for real-time voice noise reduction, wherein the generative adversarial neural network comprises a generator and a discriminator; iteratively training a generator and a discriminator based on the training data set and evaluating network performance; a generator is deployed, a noisy voice signal is obtained in real time and preprocessed, the preprocessed noisy voice is input into the generator, and a noise-reduced voice signal is obtained. According to the invention, the real-time processing requirement is met while the voice noise reduction performance is ensured.

The application relates to a voice noise reduction method, device and storage medium, wherein the voice noise reduction method comprises the following steps: acquiring a voice signal, extracting a frequency domain component of the voice signal, and dividing the frequency domain component into a high-frequency component and a low-frequency component according to the frequency size, wherein the frequency of the high-frequency component is greater than that of the low-frequency component; acquiring a sound source distance of the voice signal; and performing noise reduction on the voice signal, and in the case that the sound source distance is not lower than a preset threshold, the noise reduction intensity of the high-frequency component is not higher than that of the low-frequency component, thereby solving the problem of voice distortion when the sound source distance is far, and improving the quality of the voice signal.

The application relates to an artificial intelligence technology and discloses a voice noise reduction method based on environment detection, which comprises the following steps: obtaining environment sound in a target place collected by a first microphone, training a pre-constructed original detection model to obtain an environment detection model, obtaining noise-containing voice in the target place collected by a second microphone and determining a corresponding sound source direction, obtaining target voice of the sound source direction collected by a third microphone, detecting environment noise in the target voice by using the environment detection model, determining device noise in the target voice by using the noise-containing voice, reducing noise of the target voice by using the environment noise and the device noise, and obtaining a noise-reduced sound signal. The application further discloses a voice noise reduction device based on environment detection, an electronic device and a computer readable storage medium. The application can improve the voice noise reduction effect.

The application discloses a mini computer integrated with voice noise reduction, which comprises a mini computer body, wherein the mini computer body is provided with a microphone array comprising at least two groups of microphone units, a voice noise reduction module connected with the microphone array and an analog-digital conversion module, a central processing unit connected with the voice noise reduction module, a power amplifier module connected with the central processing unit, and an analog-digital conversion module connected with the power amplifier module and used for sampling an audio signal amplified by the power amplifier module and converting the audio signal into an echo digital reference signal. When the mini computer integrated with voice noise reduction works, on the one hand, the microphone array realizes multi-directional sound pickup, and the voice noise reduction module synchronously performs noise reduction processing, thereby effectively suppressing various noise interference; on the other hand, the voice noise reduction module eliminates external echo in mixed audio signals according to the echo digital reference signal, further accurately suppresses external echo interference, and ensures clear and pure voice quality.

The application discloses a noise suppression method and system for a microphone, and belongs to the technical field of audio signal processing. The method comprises the following steps: a main audio processing device acquires an original audio stream to obtain complex spectrum data of a current frame; real-time running state data of the audio processing device is synchronously collected; a task allocation strategy is dynamically generated; a subtask processing result is generated, and data exchange is performed; a complete full-band gain matrix for the current frame is constructed; and a time-domain audio signal after noise reduction is reconstructed through inverse transformation. The noise reduction task is intelligently decomposed and dynamically allocated to the heterogeneous computing units of the main and auxiliary devices through a cooperative scheduling mechanism for cooperative processing, so that the technical effect of realizing high-quality and low-delay real-time voice noise reduction on a resource-limited mobile terminal is achieved, and the problems of processing delay, asynchronous state updating, reduced noise reduction quality and incoherent voice caused by insufficient terminal computing power in the prior art are solved.

This invention relates to the field of speech denoising technology and discloses a multi-dimensional RNN-based speech denoising method. It acquires raw speech data with a specified preset storage threshold, removes noisy segments from the raw speech data to obtain denoising speech data, and then performs denoising processing on the denoising speech data based on a preset denoising algorithm to obtain denoised data. Finally, it iteratively trains the RNN algorithm to perform denoising processing on the denoising speech data based on the preset denoising algorithm, obtaining near-true denoised data. This method can reduce a large number of processing steps when denoising similar speech data in subsequent iterations, greatly improving the efficiency and effectiveness of the overall denoising process and enhancing the application effect of the method.

The invention discloses a self-adaptive voice noise reduction method and system in a multi-noise scene, and relates to the technical field of voice noise reduction. Comprising the following steps: S1, preprocessing input voice data, operation control training support data and wavelet analysis configuration data; s2, carrying out cross-scale structure disassembly and direction texture decomposition analysis, and carrying out prior quality constraint; s3, constructing a structural form consistent with the internal feature map of the deep network; s4, executing a structure modulation and detail coupling mechanism; s5, constructing a loss constraint system and a hierarchical supervision structure; and S6, carrying out coefficient adjustment, strategy switching and network path control. The problems that for conversation conferences and noisy environment scenes, existing noise reduction is prone to generalization and failure under non-stable noise and environment equipment changes, global energy and high-frequency details are not sufficiently considered, self-adaptive control is lacked due to time delay and computing power constraint, and consequently definition and audible identifiability are difficult to stably reach the standard are solved.

This invention discloses a mini-computer with integrated voice noise reduction, comprising a mini-computer body, on which are: a microphone array including at least two sets of microphone units; a voice noise reduction module connected to the microphone array and an analog-to-digital converter module; a central processing unit connected to the voice noise reduction module; a power amplifier module connected to the central processing unit; and an analog-to-digital converter module connected to the power amplifier module, used to sample the amplified audio signal from the power amplifier module and convert it into an echo digital reference signal. When the mini-computer with integrated voice noise reduction described in this invention is working, on the one hand, the microphone array achieves multi-directional sound pickup, and the voice noise reduction module simultaneously performs noise reduction processing, effectively suppressing various types of noise interference; on the other hand, the voice noise reduction module eliminates external speaker echo in the mixed audio signal based on the echo digital reference signal, further accurately suppressing external speaker echo interference and ensuring clear and pure voice quality.

The application discloses a single-channel speech noise reduction method and device, and the method comprises the following steps: extracting signal features of a speech time domain signal and inputting the signal features to an encoding model; dividing the signal features output by the encoding model into sub-frequency bands respectively and inputting the signal features to a frequency GRU model, and inputting the signal features divided by the sub-frequency bands and the signal features output by the frequency GRU model to a sub-frequency band processing model after splicing; the sub-frequency band processing model comprises a GRU layer, a linear layer and a one-dimensional conventional convolution layer in sequence; inputting the signal features output by the sub-frequency band processing model to a time GRU model and then to a decoding model; and outputting a speech time domain signal after optimizing and calculating the signal features output by the decoding model. The application utilizes an encoding-decoding framework and frequency point correlation, combines a recurrent neural network model, realizes speech noise reduction performance optimization, can be operated in real time on a local device, and achieves a good speech noise reduction effect.

The application provides a self-supervised speech denoising method and device, and relates to the field of acoustic signal processing, and comprises the following steps: S1, obtaining a DCUnet network, constructing a speech denoising model by adding a TCM module in the DCUnet network; S2, obtaining a noise speech set, training the speech denoising model through the noise speech set, and obtaining a trained speech denoising model; S3, denoising speech through the trained speech denoising model. The application constructs a speech denoising model based on a deep complex domain DCUnet network, combines a complex domain overall processing strategy, improves the reconstruction quality and denoising performance of the speech signal, dynamically captures the change characteristics of the noise scene through the TCM module in the speech denoising model, enhances the adaptability of the model to dynamic signals in a complex environment, trains the speech denoising model by using an ONT strategy, uses noise speech as training data, does not need clear target speech data, reduces the training data collection cost, and improves the generalization ability of the model.

The invention relates to the technical field of voice processing, can be applied to business system platforms such as financial science and technology, medical health and the like, and discloses a voice noise reduction method, device, equipment and medium, and the method comprises the steps: obtaining a to-be-processed voice signal of an audio file, and dividing the to-be-processed voice signal into multiple groups of short-time frame signals according to a preset time window; performing windowing processing on the short-time frame signals one by one to obtain windowed voice frames; performing multi-dimensional feature extraction on the windowed voice frame to obtain voice frame multi-dimensional features; carrying out feature fusion on the multi-dimensional features of the voice frames, and judging whether the windowed voice frames are effective voices or not according to the fused features and a preset threshold interval; if the windowed voice frame is a non-effective voice, deleting the windowed voice frame; and if the windowed voice frame is an effective voice, performing time sequence association optimization processing on the windowed voice frame to obtain a target voice. By dividing the voice signals, detail information in the voice is accurately positioned, and the voice processing quality is improved.

This invention discloses an adaptive speech denoising method and system for multi-noise scenarios, belonging to the field of speech denoising technology. The method includes: S1, preprocessing input speech data, operational control training support data, and wavelet analysis configuration data; S2, performing cross-scale structural decomposition and directional texture decomposition analysis, and applying prior quality constraints; S3, constructing a structural morphology consistent with the internal feature maps of the deep network; S4, executing structural modulation and detail coupling mechanisms; S5, constructing a loss constraint system and a hierarchical supervision structure; and S6, performing coefficient adjustment, strategy switching, and network path control. This invention solves the problems of existing denoising methods in noisy environments and conference calls, which are prone to generalization failure under non-stationary noise and environmental / equipment changes, insufficient consideration of global energy and high-frequency details, and lack of adaptive control due to time delay and computational constraints, resulting in unstable achievement of clarity and audibility standards.

The invention provides a voice noise reduction method and device for a hearing aid. The voice noise reduction method comprises the following steps: acquiring a sound signal based on a microphone array on the hearing aid; performing feature extraction on the sound signal based on a Transform model to obtain a feature parameter; wherein the Transform model introduces a voice activeness weight factor in a self-attention mechanism, and endows a voice segment which satisfies a preset condition with a feature enhancement coefficient which is 1.2-1.5 times of that of the voice segment; and based on the characteristic parameters, adjusting corresponding noise reduction parameters by using a dynamic noise reduction algorithm, and updating the noise reduction parameters once every preset duration. According to the method and the device, the voice activity weight factor is introduced into the self-attention mechanism, and 1.2-1.5 times of the feature enhancement coefficient is given to the voice segment meeting the preset condition, so that the real-time performance and the accuracy during voice noise reduction are improved.

The invention provides a multimedia communication method and a communication device. The multimedia communication method comprises: receiving first information from a first media network element, the first information indicating a first noise reduction service or a second noise reduction service of a second terminal, the first noise reduction service performing noise reduction on an audio stream of a first terminal and an audio stream of an opposite terminal of the first terminal by a first processing network element, the second noise reduction service is that the first processing network element performs noise reduction on the audio stream of the first terminal or the audio stream of the opposite end of the first terminal. And obtaining second information, wherein the second information indicates subscription information of the noise reduction service of the first terminal. And determining a first noise reduction mode of a second processing network element corresponding to the first terminal according to the first information and the second information. And sending first indication information to the second processing network element, wherein the first indication information indicates the first noise reduction mode. In this way, multiple redundant noise reduction can be effectively avoided, and therefore the voice noise reduction scheme of the network side is optimized.