822 results about "Speech enhancement" patented technology

The invention relates to a power grid monitoring bionic robot with multi-mode sensing and voice interaction functions. The acquisition unit acquires sound, images, temperature and power grid equipment surface micro-vibration information, completes data preprocessing and time and space calibration, and generates a sensing data packet. And the identification unit performs cross judgment based on the abrupt change characteristics and correlation of different types of data, identifies electrical, structural or environmental anomalies, and generates abnormal region description information. The construction unit analyzes whether a multi-factor coupling risk exists in combination with a power grid environment type and a historical rule, and generates task description information including processing suggestions and development path prediction. The analysis unit supports speech enhancement and tone extraction in a high-interference environment, and extracts a user interaction intention in combination with task description information. And the control unit jointly judges a behavior correction strategy according to the task description information and the interaction instruction information, generates a corresponding motion control instruction and voice feedback, and realizes autonomous monitoring and interaction response in a complex power grid environment.

A system configured to improve audio processing by performing dereverberation and noise reduction during a communication session. In some examples, the system may include a deep neural network (DNN) configured to perform speech enhancement, which is located after an Acoustic Echo Cancellation (AEC) component. For example, the DNN may process isolated audio data output by the AEC component to jointly mitigate additive noise and reverberation. In other examples, the system may include a DNN configured to perform acoustic interference cancellation, which may jointly mitigate additive noise, reverberation, and residual echo, removing the need to perform residual echo suppression processing. The DNN is configured to process complex-valued spectrograms corresponding to the isolated audio data and / or estimated echo data generated by the AEC component.

A neural end-to-end system is provided for the face and voice preserving translation of videos. The system is a pipeline of multiple models that produces a video of the original speaker speaking in the target language with modified lip movement to match the target speech, while preserving emphases and prosody of the original speech, and voice characteristics of the original speaker. The pipeline starts with automatic speech recognition including emphasis detection, followed by the translation model. The translated text is then synthesized by a Text-to-Speech model that recreates the original emphases in the target sentence. The resulting synthetic speech is then converted back to the original speakers' voice using a voice conversion model. Finally, to synchronize the lips of the speaker with the translated audio, a generative model generates frames of adapted lip movements which are combined with the audio to produce the final output. The disclosure further describes several use-cases and configurations that apply these techniques to video conferencing, dubbing, low-bandwidth transmission, speech enhancement and assistive technology for the hearing impaired.

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice enhancement method, device and equipment based on noise perception and a medium. Environment feature information is extracted and input into an audio enhancement model to generate an enhanced audio signal; obtaining a reference audio sample, extracting a personalized feature vector, and carrying out personalized processing on the enhanced audio signal; and collecting playing feedback data, determining a playing time domain adjustment parameter and a playing frequency domain adjustment parameter, adjusting the personalized enhanced audio signal, and generating an optimized audio signal. According to the method, dynamic adjustment is realized in combination with the feedback parameters in the playing process by fusing the environmental perception information and the personalized speaker characteristics, clear and natural optimized audio output with personalized styles can be generated in a complex environment, and the voice interaction quality and adaptability are improved.

A training method for a speech enhancement network, a speech enhancement method, and an electronic device. The training method comprises: performing speech effectiveness classification on respective enhanced speech frames, and on the basis of the classification results of the enhanced speech frames, generating an effectiveness distribution for sample enhanced speech; by means of the effectiveness distribution, determining the speech classification accuracy of the speech enhancement network; and measuring the degree of change in speech effectiveness of each enhanced speech frame compared to that before noise reduction, and on this basis, determining speech enhancement accuracy according to noise reduction accuracy and the speech classification accuracy. In this way, the method can focus on improving the ability of the speech enhancement network to suppress noise in non-speech segments. When the trained speech enhancement network is used to perform noise reduction on speech to be processed, if the speech comprises non-speech segments, the trained speech enhancement network can effectively reduce the phenomenon of residual noise and improve the quality of speech enhancement. The invention can be widely applied in various scenarios such as cloud technology, artificial intelligence, smart transportation, and assisted driving.

The invention relates to the technical field of speech processing, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a speech enhancement method based on multi-scale feature learning, which comprises the following steps: framing an input audio signal, extracting a Mel-frequency spectrum feature, extracting a frequency domain feature by using a multi-scale convolutional neural network, and carrying out multi-scale feature learning on the frequency domain feature; carrying out coding dimension reduction on the image; noise is suppressed through a deep residual network, enhanced audio features are generated, a non-autoregression generative model is adopted for feature conversion, and finally a generative adversarial network is used for reconstructing a target voice waveform. Voice frequency domain features are extracted through the multi-scale convolutional neural network, and the feature expression ability of different frequency bands is improved; noise suppression is carried out through a deep residual network, and the purity of the voice signals is enhanced; feature conversion is optimized through a non-autoregression generation model, and the modeling efficiency of speech enhancement is improved; the target voice waveform is reconstructed through the generative adversarial network, and the naturalness and definition of the generated voice are improved.

The invention relates to the technical field of voice enhancement, in particular to a voice enhancement method, device and system based on cross-domain feature fusion, and the method comprises the steps: extracting a to-be-enhanced voice signal, recording the to-be-enhanced voice signal as a full voice signal, and carrying out the framing of the full voice signal; determining a time domain disturbed index of the full voice signal; obtaining a frequency domain signal of the full voice signal, detecting each peak value in the frequency domain signal, and determining a mutation coefficient of each peak value; obtaining the time-frequency interference degree of the full voice signal; converting the full speech signal into a high-dimensional phase space to obtain a high-dimensional de-noised signal; and determining a threshold value in a denoising algorithm through the time-frequency interference degree, obtaining a low-dimensional denoised signal of the full voice signal by using the denoising algorithm, and obtaining an enhanced fused voice signal by combining the high-dimensional denoised signal. Therefore, the enhancement effect of the voice signal is improved.

The invention discloses a multi-mode speech enhancement system based on audio and video, and the system comprises a data collection module which collects target audio, background noise and interference sound through a microphone; a camera is used for synchronously collecting environment visual information, including facial expressions and mouth shapes of speakers, and noise and interference sounds are normalized to ensure that the noise and the interference sounds are matched with the dynamic range of a target audio; the data analysis module is used for preprocessing the collected audio, including noise suppression and speech enhancement, and improving the speech definition by applying a deep learning model; through the noise reduction and voice enhancement technology, the interference of background noise can be effectively reduced, and the voice is clearer. Thus, audiences can more easily understand speech contents, especially in noisy environments, such as public transport means or busy streets; and the voice recognition accuracy is improved, and the quality of the voice signal can be improved.

A method for performing denoising on audio signals is provided. In some implementations, the method involves determining an aggressiveness control parameter value that modulates a degree of speech preservation to be applied. In some implementations, the method involves obtaining a training set of training samples, a training sample having a noisy audio signal and a target denoising mask. In some implementations, the method involves training a machine learning model, wherein the trained machine learning model is usable to take, as an input, a noisy test audio signal and generate a corresponding denoised test audio signal, and wherein the aggressiveness control parameter value is used for: 1) generating a frequency domain representation of the noisy audio signals included in the training set: 2) modifying the target denoising masks: 3) determining an architecture of the machine learning model: or determining a loss during training of the machine learning model.

The invention relates to the technical field of voice processing, in particular to a mixed voice processing method, electronic equipment and a computer readable medium. The method comprises the following steps: collecting mixed voice and environment influence parameters; carrying out bionic frequency domain analysis on the mixed voice to obtain low-frequency attenuation compensation characteristic data; performing multipath effect propagation analysis on the low-frequency attenuation compensation characteristic data through the environmental influence parameters to generate channel distortion data; performing time domain-frequency domain joint deconvolution processing on the mixed voice by using the channel distortion data to generate a direct sound component and a reflected sound component; performing adversarial training based on the direct sound component and the reflected sound component to generate anti-multipath speech enhancement data; and constructing a dynamic frequency compensation filter based on preset environmental acoustic characteristics. Through the multi-stage signal processing, frequency compensation and real-time optimization technology, the output quality of the mixed voice is improved.

The invention discloses a speech enhancement method and system based on a time-frequency graph convolutional network in the technical field of speech signal processing, and the method comprises the steps: obtaining to-be-enhanced noisy speech data, inputting the to-be-enhanced noisy speech data into an improved metric generative adversarial network (CMGAN), and outputting an enhanced speech time domain signal; the improvement method of the metric generative adversarial network CMGAN comprises the following steps: replacing an encoder module of the metric generative adversarial network CMGAN with a time-frequency graph convolution encoder module; in the time-frequency graph convolution encoder module, a time convolution network and a graph convolution network are used for respectively extracting a long-time dependency relationship and global frequency characteristic information of noise-containing voice data to be enhanced, and dense residual error connection is introduced to fuse multi-scale characteristic information at the same time. The speech enhancement performance is improved under the condition of less model parameter quantity and calculation quantity.

The invention provides a streaming speech recognition method, device and equipment based on an adaptive AI large model, and the method comprises the steps: carrying out the noise reduction and speech enhancement of a collected streaming speech signal, and obtaining a preliminary speech signal; performing feature extraction on the preliminary voice signal to obtain a voice feature vector; performing multi-layer perceptron coding on the voice feature vector to obtain an initial coding vector; dynamically adjusting the clustering center and the clustering number of a clustering algorithm according to the distribution change of the voice feature vector, so as to cluster the initial coding vector, and obtaining a clustered coding vector; dynamically allocating weights for the clustered coding vectors according to voice context information through an attention mechanism of adaptively adjusting the weights, and performing weighting processing; and inputting the weighted coding vector into a neural network based on an adaptive growth structure for decoding to obtain a speech recognition result. According to the invention, the quality of a speech recognition result is effectively improved.

The invention belongs to the technical field of speech enhancement, and discloses a Schrodinger bridge-based diffusion model speech enhancement method and system, and the method comprises the steps: converting a diffusion process of a diffusion model into a solving process of a stochastic differential equation, and determining the stochastic differential equation according to the theoretical principle of a Schrodinger bridge, and the complex spectrum is directly used as the input of the diffusion model, so that the tedious work of extracting a phase spectrum and an amplitude spectrum from the complex spectrum and the memory overhead caused by inverse transformation are omitted, and meanwhile, the alignment problem between the phase spectrum and the amplitude spectrum is also avoided. According to the speech enhancement method, unique features of time sequence signals are captured through a Transformer module, multi-scale information is fused through a U-Net module, a loss function covering a time domain, a frequency domain and a time-frequency domain is matched, the difference between a prediction sample and a clean sample is gradually reduced, the nonlinear diffusion process from a noisy sample to the clean sample can be directly learned, and the speech enhancement accuracy is improved. Therefore, the structure information of more initial samples is reserved.

The invention discloses an unmanned aerial vehicle speech enhancement method and system based on harmonic features and deep learning, and the method comprises the following steps: collecting a rotor self-noise signal of an unmanned aerial vehicle, namely, an original sampling frequency, processing the original sampling frequency, obtaining a feature vector, processing the feature vector, and generating a noise dynamic change label. Combining the feature vector and the noise dynamic change label into a data set, constructing a deep learning model, inputting the data set into the deep learning model for processing, outputting a prediction result of the noise dynamic change label, extracting the collected data to obtain a noisy speech amplitude spectrum, smoothing the noisy speech amplitude spectrum, and obtaining a noise dynamic change label. And obtaining an enhanced signal, and outputting the enhanced signal. According to the method, the self-noise signal of the rotor wing of the unmanned aerial vehicle is extracted, and the noise change trend is predicted in combination with the deep learning model, so that the noise and the voice can be separated more accurately, and the voice signal-to-noise ratio is greatly improved.

The invention relates to the technical field of wireless communication, and discloses a low-power-consumption communication method and device for wireless audio equipment, an SoC chip and a storage medium. The method comprises the following steps: sampling acoustic characteristics of heterogeneous audio data of a plurality of target audio devices, calculating a voice spectrum corresponding to device characteristics, and extracting multi-device acoustic characteristics, performing multi-frequency sub-band speech enhancement, heterogeneous segmentation and grouping of multi-target audio equipment, verification value calculation and differential coding on each extracted acoustic feature sequence to obtain a multi-source coding speech segment; performing transmission unit division and cooperative signal modulation, cooperative resource allocation and data stream transmission of each transmission unit on the multi-source coding voice segment to obtain a transmission voice data stream; and obtaining a transmission voice data stream transmitted with low power consumption and performing audio recovery on the obtained data to obtain a target audio data stream transmitted by the multi-target audio equipment. The communication power consumption of the multiple target audio devices is reduced, and the audio transmission quality is ensured.

The invention belongs to the technical field of speech enhancement, and particularly relates to a lightweight speech enhancement method based on a dual-path one-dimensional convolution group loop network. The method comprises the following steps: firstly, acquiring a noisy voice signal, and extracting a spectrum feature of the noisy voice signal; then, constructing a dual-path one-dimensional convolution group cycle network model which comprises an encoder module, a decoder module and a dual-path grouping cycle module located between the encoder module and the decoder module, and performing complex multiplication on the output characteristics of the decoder module and the input characteristics of the encoder module to obtain an estimated complex ratio mask; and finally, extracting the spectrum characteristics of a to-be-enhanced noisy voice signal, and inputting the spectrum characteristics of the to-be-enhanced noisy voice signal into the trained model to obtain an estimated complex ratio mask. Performing complex multiplication on the spectrum characteristics of the noisy voice signal to be enhanced and the estimated complex ratio mask to obtain the spectrum characteristics of the enhanced voice signal; and performing inverse short-time Fourier transform and overlap-add on the spectrum characteristics of the enhanced voice signal in sequence to obtain the enhanced voice signal. According to the method, the model parameter quantity and the calculation quantity are reduced, and the problems that an existing voice enhancement technology is insufficient in real-time processing performance, large in calculation resource demand and not suitable for being deployed on edge equipment are solved.

The invention discloses a remote speech enhancement transmission method and system based on semantic communication in the technical field of speech signal transmission processing, and the system comprises a local transmitting end which is used for carrying out the short-time Fourier transform of a to-be-enhanced noisy speech signal, and then carrying out the semantic feature extraction of the frequency spectrum of the noisy speech signal through a semantic encoder; carrying out dimension adjustment on the voice semantic features by using a channel encoder; and finally, transmitting to a remote receiving end through a channel. The remote receiving end is used for receiving the voice semantic feature signal transmitted through the channel; after dimension recovery is carried out on a received voice semantic feature signal by utilizing a channel decoder, semantic decoding is carried out by utilizing a semantic decoder to obtain a real part and an imaginary part of a predicted pure voice signal, and finally inverse short-time Fourier transform is carried out to obtain a reconstructed voice signal. According to the invention, under the condition of saving communication resources, the quality of pure voice recovered by a remote receiving end under a low signal-to-noise ratio channel transmission condition can be obviously improved.

The invention discloses a self-adaptive voiceprint recognition system based on a Mama model. The self-adaptive voiceprint recognition system comprises a voice acquisition module, a voice enhancement module, a voice feature extraction module, a spectrogram enhancement module, a model training module and a model decision module, according to the invention, through well-designed network architecture and algorithm optimization, high identification accuracy is ensured, and good robustness is shown; the system can keep a low equal error rate under various noise conditions and in different speaking styles, which indicates high robustness and accuracy of the system in a voiceprint recognition task; a bidirectional state space model of the Mama module can effectively capture local details and global context of voiceprints, and the recognition capability of the system on complex voice features is enhanced; the TDNN module strengthens the sensitivity of the system to time sequence features, the DenseNet module improves the transmission and fusion of the features through the dense connection of the DenseNet module, and the combination of the TDNN module and the DenseNet module effectively improves the feature extraction fineness of the Mamba module and improves the robustness of the model.

The embodiment of the invention discloses a speech enhancement method and device based on a generative adversarial network model, and electronic equipment, and can solve the problems of how to more accurately and effectively reduce the noise influence and improve the definition and quality of an audio signal. The method comprises the following steps: acquiring to-be-processed voice data; performing convolution processing on the to-be-processed voice data by generating a target generator in the adversarial network model to obtain initial voice data, the target generator comprising an expansion convolution layer and a standard convolution layer, and the target generator being obtained by co-training an adversarial loss function, a perception loss function and a reconstruction loss function; through a target discriminator in the generative adversarial network model, the initial voice data and the to-be-processed voice data are discriminated to obtain a target discrimination result, and the target discriminator comprises a time domain discriminator and a frequency domain discriminator; and when it is detected that the target discrimination result satisfies a preset requirement, determining that the initial voice data is voice data after voice enhancement of the to-be-processed voice data.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

A method and apparatus comprising computer code configured to cause a processor or processors to receive an audio signal obtained from a microphone, input the audio signal into a neural-network pipeline, the neural-network pipeline including a convolutional network that receives the audio signal and provides a first output of the convolutional network to an enhancer, the enhancer including a deep complex convolutional recurrent network that receives the first output along with a mel spectrogram of the audio signal and outputs a second output to at least one of a vocoder and a decoder, and control an output of an enhanced audio signal from the at least one of the vocoder and the decoder.

The invention provides a single-channel speech enhancement method based on an improved convolutional recurrent network, and the method comprises the steps: 1, downloading a data set for model training and testing, carrying out the preprocessing of the speech of the data set through employing a VoiceBank + DEMAND data set, and extracting the amplitude spectrum of a speech signal through short-time Fourier transform (STFT); 2, inputting the amplitude spectrum into an encoder, and extracting high-dimensional features from the amplitude spectrum; step 3, using the Channel-S4D block as a recursion module of the model, inputting a result output by the Channel-S4D block into a decoder, and recovering the result to an original dimension; 4, constructing a joint loss function; and 5, reconstructing and evaluating the enhanced voice signal. According to the invention, the Channel-S4D module constructed based on the diagonalization state space model efficiently processes a voice sequence, captures channel-level voice key information, better models the time dependence relationship of voice signals, and improves the adaptability of the model to a complex noise environment while reducing the parameter quantity and the calculation quantity of the model.

The invention discloses a property service response method and system based on a voice robot, and relates to the technical field of voice recognition, and the method comprises the steps: collecting a voice signal, generating a spectrogram feature, generating keyword category prediction based on the spectrogram feature, and carrying out the signal screening according to a prediction result; and positioning a signal sound source direction based on a screening result, extracting a target voice signal, performing voice enhancement on the target voice signal, converting the voice into a text, performing semantic analysis on the text, and generating a user instruction. Through voice signal acquisition and adaptive noise suppression of the multi-channel microphone array, the signal acquisition precision is improved, based on an improved spectrogram feature generation mechanism, the feature expression ability of voice signals is improved, and the service response speed and accuracy are remarkably improved.

Disclosed in the present application is a method for training a speech enhancement network, the method comprising: acquiring a training set, wherein the training set comprises a plurality of training samples, and each training sample comprises sample reference speech, sample comparison speech and mixed speech; performing voiceprint extraction on the sample reference speech to obtain a sample voiceprint vector; performing audio feature extraction on the mixed speech to obtain a sample audio feature; performing, by means of a speech enhancement network, enhancement processing on the basis of the sample voiceprint vector and the sample audio feature, and outputting a predicted audio feature for a sample sound-producing object; on the basis of the predicted audio feature and a comparative audio feature corresponding to the sample comparison speech, determining a training loss value of the speech enhancement network; and iteratively updating weight parameters of the speech enhancement network on the basis of the training loss value until a training ending condition is met. The present application is applied to artificial intelligence, and by means of the present application, a speech enhancement network for suppressing interfering human voices can be trained on the basis of sample reference speech and sample comparison speech which originate from the same sound-producing object, thereby effectively improving the quality of speech enhancement.

The invention discloses a speech enhancement method and system based on a selective state space model Mama, and the method comprises the steps: carrying out the short-time Fourier transform of a distorted speech, obtaining a time-frequency domain signal, splicing the amplitude, real part and imaginary part of the time-frequency domain signal into a signal of three channels, and enabling the signal to serve as an input signal; an input signal is expanded from three channels to a plurality of channels through a first convolution block of a generator network encoder, different resolution characteristics are extracted through an expanded dense convolution layer of the encoder, a receptive field is increased, and the frequency dimension of the signal is reduced to 1 / 2 of the original frequency dimension by using the last convolution block of the encoder so as to reduce the calculation complexity. The signals expanded by the encoder simulate forward and backward dependency relationships of voice signals under different resolutions through N TF-mamba blocks so as to capture local and global features of long-sequence voice signals, and feature enhancement is realized; and the amplitude and the phase of the enhanced signal are predicted through a complex decoder and a mask decoder to obtain enhanced speech after denoising and reverberation removal.

The invention discloses a multi-channel speech enhancement method based on multi-scale spatial information and spectrum feature fusion, which comprises the following steps: recombining different spectrum components according to spectrum features, and extracting in-channel, inter-channel and full-channel feature modes; the features are fused, and unified depth features are created; a local feature extraction module is introduced to enhance the feature weight of the current frame, and features under different scales are fused through a feature attention mechanism; a decomposition attention mechanism is introduced, and encoder and decoder outputs are fused in a plurality of decomposition spaces, so that detail features can also be used by a depth module. According to the method, spatial features and spectrum features are combined, and unified feature expression is created by using a feature fusion method. The attention module adaptively learns modes contained in the spatial features and utilizes the modes instead of fitting directional features with physical meanings, so that the method can adapt to different scenes more flexibly, and has a good application prospect.

The invention discloses a display equipment intelligent voice interaction method based on a large language model, and the method comprises the steps: constructing a set of display equipment intelligent voice interaction system suitable for a complex scene based on a large language model technical framework through integrating a multi-stage audio processing module, an intelligent voice recognition module, a semantic understanding and intention recognition module and a multi-mode interaction control module; the voice enhancement technology is adopted, high-precision voice instruction capture and noise reduction processing can be achieved in a noisy environment, and geographic space parameters and operation logic implied in user intentions are accurately analyzed in combination with a deep semantic analysis engine; through a multi-mode fusion control mechanism, real-time linkage interaction between a voice instruction and display equipment is supported, natural and smooth voice control experience is provided especially for scenes such as three-dimensional space positioning and topographic data calling in a holographic geographical environment, and the system has strong error recovery capability and good expansibility and is suitable for being popularized and applied. The method is especially suitable for a voice control interaction scene in a holographic environment.

The invention relates to the field of aviation voice signal processing, and discloses a voice recognition compensation pilot earphone hearing protection method and system, and the method comprises the following steps: collecting multi-modal data, and separating a sound source through tensor decomposition; inferring a pilot state by using a dynamic network; performing context recognition and semantic evaluation on the attention target voice; and finally, dynamically modulating the sound field based on deep reinforcement learning, enhancing the voice in a personalized manner, and outputting after noise suppression. The system comprises a multi-mode perception data acquisition module, a sound source decoupling module, a pilot state inference module, a voice processing and semantic evaluation module and a sound field modulation and output module. According to the invention, high-fidelity speech extraction is realized through multi-modal perception and tensor decomposition; evaluating priority key information in combination with attention and semantics; and deep reinforcement learning and model prediction control are adopted to dynamically optimize the sound field, so that the voice recognition accuracy and the pilot information acquisition efficiency are improved.

The invention relates to the technical field of voice recognition, in particular to an implementation method and device of a multi-channel voiceprint recognition system, and the implementation method comprises the steps of multi-channel data acquisition and synchronization, signal preprocessing and enhancement, feature extraction and fusion, model training, real-time deployment and adaptive optimization. Compared with the problems that a traditional multichannel voiceprint recognition system depends on a fixed beam forming algorithm and an independent clock synchronization module, the synchronization error is large, manual parameter adjustment is needed for noise suppression, and generalization is poor, hardware-level clock synchronization is achieved through a PTP protocol, and the accuracy of noise suppression is improved. The method combines an end-to-end neural network to automatically learn noise distribution and a sound source space position, dynamically generates a beam forming weight, can improve the voice quality in a complex noise scene without manual intervention, remarkably reduces the interference of a synchronization error on sound source positioning, and enables the precision and stability of far-field voice enhancement to reach a new level.