39 results about "SPEECH DISTORTION" patented technology

The invention belongs to the technical field of speech enhancement, and particularly relates to a controllable causal double-path speech enhancement method and system, and the method comprises the steps: taking noisy speech collected in an actual scene as input, carrying out the short-time Fourier transform, and obtaining a complex spectrum containing an amplitude spectrum and a phase spectrum; constructing a streaming encoder, taking the complex spectrum as input, and extracting local correlation of the noisy speech in a time dimension and a frequency dimension; a controllable causal double-path module is constructed, long-term dependency relationship modeling is carried out on the time dimension and the frequency dimension, and linear modulation is carried out on channel characteristics through control parameters; constructing a streaming amplitude / phase decoder, and reconstructing an amplitude spectrum and a phase spectrum of the enhanced speech; and performing short-time inverse Fourier transform by combining the reconstructed amplitude spectrum and phase spectrum to obtain the waveform of the enhanced speech. According to the invention, the real-time adjustment of the output characteristics of the voice enhancement network is realized, and the noise residue and the voice distortion level can be timely balanced according to the demands of a listener in the voice enhancement process.

The invention discloses a low-complexity cascaded generalized sidelobe cancellation beam forming method and device, and the method comprises the steps: decomposing a high-dimensional generalized sidelobe cancellation device into two low-dimensional generalized sidelobe cancellation units which are cascaded front and back through constructing a beam former of a cascaded generalized sidelobe cancellation structure; according to the method, the algorithm complexity can be remarkably reduced, the robustness is improved, the real-time performance of the algorithm is improved by utilizing a front-back stage feedback iteration processing mode based on the frame, and the method has higher practical value. According to the method, an interference noise cancellation module is constructed by further utilizing the idea of multi-channel Wiener filtering, filtering coefficients are directly updated between frames, additional noise estimation is not needed, the method is easy to implement and high in robustness, and interference noise suppression and target voice distortion can be balanced. In addition, through a multi-branch fusion and interleaving strategy, the degree of freedom of beam weight coefficient search can be improved, the algorithm can find an optimal solution more easily, and the target enhancement and interference noise suppression performance is improved.

The invention relates to a noise reduction method and device, a mobile terminal and a medium, and is applied to the mobile terminal, and the method comprises the steps: obtaining a sound signal with noise; obtaining the noise intensity of the steady-state noise signal based on the noise-containing sound signal and a noise reduction model; based on the noise intensity of the steady-state noise signal and the first estimator gain function, removing the steady-state noise signal from the noise-containing sound signal to obtain the noise intensity of the target sound signal; and obtaining the target sound signal based on the noise intensity of the target sound signal. The noise estimation of the steady-state noise signal is carried out by using the pre-stored noise reduction model, the speed and accuracy of noise estimation can be effectively improved, a better noise reduction effect is obtained, the problems of noise residual, voice distortion, unstable volume and the like caused by inaccurate noise estimation are avoided, the learning difficulty of the noise reduction model is low, and the noise reduction efficiency is improved. The calculation amount of the noise reduction process is reduced, and the applicability of the noise reduction method is improved.

The invention discloses a robust speech recognition method and system based on multi-stage feature fusion, and relates to the technical field of speech recognition. The invention provides a speech recognition model for robust speech recognition, which comprises the following steps: firstly, extracting a magnitude spectrum | Y | from noisy speech Y through a speech coding part, coding the magnitude spectrum | Y | into a preliminary feature Ybasic, then, processing the Ybasic through a speech enhancement part to obtain a hidden feature Yhidden, a masking feature Ymask and a mapping feature Ymap, and finally, carrying out speech recognition on the hidden feature Yhidden, the masking feature Ymask and the mapping feature Ymap. Then, a fusion feature Ffuse is obtained through three-stage feature fusion, and finally, the Ffuse is decoded through a voice decoder to obtain Result. According to the model, feature complementation, feature interaction and high-low layer semantic alignment in the process are enhanced, the problem of voice distortion introduced by voice enhancement is systematically relieved, information loss in different stages is solved, and therefore the final voice recognition effect is guaranteed.

The invention discloses an audio noise reduction system, a noise reduction method and a vehicle-mounted audio system, and the noise reduction system comprises an input audio preprocessing module which carries out the preprocessing of an input audio signal, so as to generate an audio frequency spectrum; the deep neural network separation module is configured with a feature extraction layer which is used for extracting audio features, namely time features and frequency features, from the audio spectrum; the encoder is used for compressing the audio features output by the feature extraction layer and generating potential spatial representation according to the audio features; the decoder is used for reconstructing and separating human voice and noise according to the potential space representation; the self-adaptive mask generator is used for generating a time sequence frequency spectrum mask according to the frequency difference between the noise and the human voice in combination with the time characteristic so as to separate the human voice audio; and the output reconstruction module is configured to carry out inverse transformation on the frequency characteristics of the separated human voice audio signals so as to reconstruct and obtain time domain audio data, so that different noise types can be adapted, and voice distortion or error suppression can be avoided.

The invention provides an ultra-low time delay signal sequence conversion method and system, which can be widely applied to various time sequence conversion scenes, including hearing enhancement systems and equipment, such as hearing aid, denoising, simultaneous translation, sound conversion signal sequence, intelligent glasses, brain-computer systems and the like. According to the invention, the rapid high-fidelity noise elimination method is adopted, so that the limitation and difficulty in the traditional technology can be overcome. And for the problems of noise uncertainty and voice distortion in the sound related field, the high-quality voice can be recovered by recognizing the voice content as the intermediate language representation with high generalization, and meanwhile, the voice of the same speaker is synthesized by using a pre-trained artificial intelligence module. And the ultra-low time delay is realized by using a variable multi-partition block and a multi-head prediction mechanism in the process. Therefore, in the specific hearing enhancement field, the voice of the target speaker can be focused by using the voice separation technology, and the uncertainty of background noise is avoided.

The present invention discloses a speech enhancement method, device, equipment and storage medium. Acquire speech sample data, and split the speech sample data into a training set and a verification set according to a set ratio; wherein the speech sample data includes noisy speech and corresponding pure speech; train a set speech enhancement model based on the training set; verify the trained set speech enhancement model based on the verification set; input the speech to be enhanced into the verified set speech enhancement model, and output the target speech. The speech enhancement method provided in an embodiment of the present invention inputs the speech to be enhanced into the verified set speech enhancement model, and outputs the target speech, which can improve the problem of speech loss and speech distortion that easily damage the original speech data during the speech enhancement process, and ensure the effect of speech enhancement.

The invention provides a voice interaction method and device, equipment, a medium and a program product. The voice interaction method comprises the following steps: determining a first confidence coefficient of a first original signal and a second confidence coefficient of a first voice enhancement signal; wherein the first voice enhancement signal is a signal obtained by performing voice enhancement on the first original signal; determining a target signal based on the first confidence coefficient, the second confidence coefficient, the second original signal and the second speech enhancement signal; wherein the second voice enhancement signal is a signal obtained by performing voice enhancement on a second original signal; and performing voice interaction with the target device based on the target signal. According to the invention, voice distortion caused by voice enhancement can be reduced.

The invention discloses a Flow-Flow architecture-based voice hiding method and system, a medium and equipment in the technical field of information hiding, and aims to solve the optimization problem of the existing information hiding technology. The method comprises the following steps: acquiring a Mel spectrogram of a to-be-hidden carrier text; mapping the secret voice to a hidden variable by using a FlowMap mapper; according to the hidden variable and the Mel spectrogram of the to-be-hidden carrier text, voice hiding is carried out through a FlowVocoder voice generator, and secret-carrying voice is obtained; performing text format conversion on the encrypted voice to obtain a Mel spectrogram of the encrypted voice in the text format; restoring the Mel spectrogram of the encrypted voice and the Mel spectrogram of the encrypted voice in the text format into hidden variables through a FlowVocoder voice inverter; and mapping the restored hidden variables into restored secret voice by using a FlowMap inverse mapper. According to the invention, the problem of secret voice extraction quality reduction caused by secret-carrying voice distortion is solved.

The application discloses a speech recognition method based on self-supervised pre-training and an interactive fusion network, constructs a speech recognition model, uses a self-supervised pre-training model as a feature extraction part after a speech enhancement module, effectively combines the speech enhancement module and the self-supervised pre-training method, and relieves speech distortion caused by speech enhancement; an interactive feature fusion method is used to fuse enhanced features and original audio features, so that information loss in the speech enhancement process is made up. By using the method, low-resource speech recognition results are more accurate, and the recognition accuracy of low resources in a complex environment is improved.

The present application relates to the technical field of short wave frequency band communication equipment, in particular to an automatic control system of audio output amplitude and a method thereof. The present application is convenient to build and easy to realize; by audio sampling, the present audio output amplitude and the change trend are actively identified, and the amplifier inside the DSP chip is controlled to dynamically compensate the audio digital signal sent to the speech compression circuit, so that the audio output amplitude meets the index requirement, the small short wave audio output amplitude change is ensured in the extreme environment, the speech distortion degree is reduced, the speech quality of the short wave communication is improved, and the communication requirement in the extreme environment is met.

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 invention relates to the technical field of voice processing, can be applied to the fields of finance and medical treatment, and discloses a language enhancement method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an input voice signal with noise; the input voice signal with noise is converted into noise embedded data through a pre-trained generative audio encoder; performing denoising processing on the noise embedded data through a denoising encoder to obtain clean embedded data; and converting the clean embedded data into an enhanced target voice signal through a pre-trained vocoder. According to the method, the naturalness of the enhanced voice and the consistency of the speaker are effectively improved, the modeling difficulty of complex noise distribution is reduced, the voice distortion is reduced, meanwhile, the model parameter quantity and the training complexity are greatly reduced, the reasoning speed is improved, and the real-time application can be realized in a low-resource environment.

This application discloses a voice enhancement method, device, and equipment. Among them, the method obtains an acoustic feature enhancement model through an adversarial multi-task learning method with a self-supervised noise classification loss, and determines the enhanced acoustic features of noisy speech through this model, which can avoid being sensitive to environmental noise when extracting enhanced acoustic features; therefore, it can effectively narrow the difference in voice enhancement performance among various environmental noises and improve the generalization ability of noises outside the training set. In addition, since this processing method performs voice synthesis on the enhanced acoustic features through a vocoder to obtain the enhanced voice of the noisy speech, avoiding directly or indirectly enhancing the phase spectrum of the noisy speech; therefore, it can effectively reduce speech distortion and improve the listening quality of the speech.

The invention provides a live broadcast sound card noise reduction method based on environment feature recognition, relates to the technical field of noise reduction, and can realize dynamic adaptation and precise processing of multiple types of noise and reverberation factors in a live broadcast scene by constructing a noise reduction process oriented to environment feature recognition, thereby remarkably improving the definition and the reduction degree of voice signals. In particular, in a complex or violently changing live broadcast environment, intelligent identification can be carried out based on multiple parameters such as reverberation time, environmental noise energy and background spectrum entropy characteristics, and it is ensured that the noise reduction process has pertinence and real-time performance. Through a multi-stage processing strategy, reverberation tail sound is effectively suppressed, and residual noise is adaptively recognized and filtered, so that the common problem of voice distortion or detail loss in a traditional noise reduction method is avoided.

The embodiments of the present application provide a method, apparatus, and device for training a speech enhancement model, which relates to the field of speech processing and recognition technology and is used to reduce speech distortion and suppress noise in audio through a speech enhancement model. In this method, a training sample set is first obtained; the first audio sample in the training sample set is preprocessed through the input layer of the speech enhancement model to obtain first audio sample data; the audio features of the first audio sample data are extracted through N hidden layers of the speech enhancement model; the audio features are respectively input into M output layers of the speech enhancement model to obtain M audio noise reduction results; the losses between the M audio noise reduction results and the audio masking results are determined through the loss functions corresponding to the M output layers to obtain M loss values; and the network parameters of the input layer, N hidden layers, and M output layers are adjusted according to the weighted results of the M loss values ​​to obtain a trained speech enhancement model.

The invention discloses a voice instruction recognition method and system with noise robustness. The method comprises the following steps: S1, constructing a training data set; s2, constructing a noise robustness voice instruction recognition model and training the model; and S3, processing actual noisy voice by using the noise robustness voice instruction recognition model trained in the step S2 so as to execute instruction recognition operation. Wherein the constructed noise robustness voice instruction recognition model comprises a voice enhancement model used for carrying out noise reduction processing on input voice with noise and outputting enhanced voice; the voice distortion sensor is used for outputting a distortion probability according to the voice enhancement model; the voice recognition model is used for processing the fused enhanced voice and outputting a voice instruction; according to the method, the voice distortion perceptron is arranged in the model to obtain the distortion probability in real time, difference mixing is carried out on the audio, voice distortion is reduced, and the robustness and accuracy of the whole system are further improved.

The invention discloses a real-time single-microphone voice noise reduction algorithm based on voice enhancement residual error and continuous spectrum estimation, and relates to a real-time single-microphone voice noise reduction algorithm. The invention aims to solve the problem that conversation voice is buried by noise due to noisy and diverse background noise of an interphone in a special communication scene. Noise power spectrum estimation is optimized by fusing a continuous minimum value tracking algorithm, a speech enhancement residual error is introduced as a real noise approximate value to participate in a recursive average process, and the response speed and accuracy of noise estimation are improved; a gain function is calculated in combination with an optimal correction logarithm MMSE estimator, and a closed expression approximates exponential integration to reduce the calculation complexity. The algorithm specifically comprises the steps of preprocessing and framing, noise power spectrum estimation, gain function calculation, voice reconstruction and the like, the segmentation signal-to-noise ratio is remarkably improved on the premise that low voice distortion is guaranteed, and the real-time processing requirement is met. The invention belongs to the technical field of voice signal processing.

The present application proposes a speech enhancement method, device, equipment and storage medium, the method comprising: inputting noisy speech into a pre-trained composite adversarial enhancement network model, obtaining a speech enhancement result output by the composite adversarial enhancement network model, the speech enhancement result at least including enhanced speech; wherein the voiceprint features of the enhanced speech obtained by the composite adversarial enhancement network model performing speech enhancement processing on the noisy speech are close to the voiceprint features of the clean speech corresponding to the noisy speech. The above method can reduce the speech distortion caused by speech enhancement while performing speech enhancement on the noisy speech.

The application relates to the technical field of medical instruments, and discloses a ward round vehicle medical patient communication information online synchronization system, which comprises a data acquisition module, a data preprocessing module, a sound quality analysis module, a tone analysis module, a patient comprehensive analysis module, a report review module and a report generation module. Compared with traditional ward round technology, the system realizes comprehensive intelligentization and dataization of the ward round process through the integrated multi-module collaborative work. In terms of sound quality processing, the system solves the speech distortion problem in a high-noise environment through dynamic noise reduction and audio enhancement technology; in terms of dialect recognition, the system significantly improves the accuracy of dialect communication through tone analysis technology; in terms of report generation, the system greatly improves the efficiency and accuracy through intelligent decision-making and automatic generation mechanism. Overall, the system optimizes the ward round process, improves the medical patient communication quality, reduces the medical error rate, and provides more reliable support for clinical decision-making.

The invention relates to a method, a system and equipment for compressing a speech synthesis model, and relates to the field of artificial intelligence. According to the technical scheme, the problem of traditional model deployment is solved, storage in an ONNX format occupies 112 MB originally, the hardware requirement is high, embedded equipment is difficult to bear, the model is only 30 MB after compression, reasoning is accelerated, and smooth operation can be achieved on edge equipment such as an ARM chip and a low-power-consumption MCU; moreover, the scheme is superior to a traditional compression means, solves the problem of proneness to voice distortion caused by static quantization and violent modification of a model structure, adopts quantization perception training to control errors, and ensures that voice details and tone quality are basically flush with those of an original model; in addition, according to the scheme, the training cost is reduced, 1000 epochs can reach the target on the basis of the original pre-training model, the GPU investment is reduced, the GPU cycle is shortened, and the threshold of enterprises and developers is reduced.

This invention relates to the field of audio signal processing technology, specifically to a directional sound pickup and adaptive noise reduction system and method using a multi-microphone array. The invention acquires signals through a multi-microphone array and performs time-frequency conversion. A beamforming unit forms a directional beam according to the target direction and constructs spatial nulls in the interference direction. A noise reduction unit adaptively suppresses noise in the beam output, simultaneously extracting residual noise energy and speech distortion feature values. A closed-loop feedback control unit executes a zero-point adaptive optimization algorithm based on the joint constraints of residual noise energy and speech distortion. Using the aforementioned feature values, a cost factor is calculated and a beam zero-point direction update is generated, which is fed back to the beamforming unit to achieve closed-loop adaptive adjustment of the null direction. This breaks the limitation that traditional beamforming and post-noise reduction are independent, and can simultaneously improve noise suppression capability and speech fidelity in complex acoustic environments.

This invention provides a new and improved hearing aid system with high quality noise cancellation method and devices to overcome the limitations and difficulties encountered in conventional technologies. The technical limitations of the noise uncertainty and speech distortion in the hearing aid field are resolved by restoration of the high-quality speech by converting the speech content into an intermediate linguistic representation and by synthesizing the speech of the same speaker with pre-trained using artificial intelligence (AI) modules. In this invention, the noise uncertainties are circumvented by focusing on the target speaker or picking up the dominant speech by choosing the corresponding setting assuming the speech from the target speaker is the dominant speech based on the Lombard effect.

The present invention discloses an audio noise reduction system, a noise reduction method and a vehicle-mounted audio system. The noise reduction system comprises an input audio preprocessing module, which preprocesses an input audio signal to generate an audio spectrum; a deep neural network separation module, which is configured with: a feature extraction layer, which extracts audio features, namely, time features and frequency features, from the audio spectrum; an encoder, which is used to compress the audio features output by the feature extraction layer and generate a latent space representation according to the audio features; a decoder, which reconstructs and separates human voice and noise according to the latent space representation; an adaptive mask generator, which generates a time-series spectrum mask according to the frequency difference between noise and human voice and the time feature to separate the human voice audio; and an output reconstruction module, which is configured to inversely transform the frequency features of the separated human voice audio signal to reconstruct time-domain audio data, which can adapt to different noise types and avoid speech distortion or erroneous suppression.

The invention provides an earphone communication connection method and device. The method comprises the following steps: receiving noisy voice data; performing time-frequency decomposition and voice feature extraction on the data to obtain an amplitude spectrum of the voice data with noise, and identifying noise features of the amplitude spectrum; obtaining a similarity coefficient corresponding to the noisy voice data based on the component proportion of expected voice data and noise data in the noisy voice data; adjusting the time-frequency mask based on the similarity coefficient to obtain a target mask; and obtaining enhanced voice data based on the target masking. A similarity coefficient is introduced, the energy proportion of noisy voice and noise and the energy proportion of the noisy voice and target voice are adjusted from the angle of human ear perception, and the masking value of each time frequency unit is adjusted, so that the trained target masking is closer to the masking value of pure voice under the low signal-to-noise ratio, and the training accuracy is improved. Interference noise is removed, meanwhile, the problem of voice distortion caused by excessive noise removal in ideal ratio masking is avoided, and more target voice components are effectively reserved.

The invention relates to the technical field of voice processing, and discloses a voiceprint-driven voice noise reduction method and terminal equipment, and the method comprises the steps: collecting a terminal user voice sample, and generating a user exclusive voiceprint packet; collecting an audio input by a user, analyzing the input audio through an AI algorithm, separating voiceprint feature information in the audio, and obtaining input voiceprint data; performing similarity comparison on the input voiceprint data and a user exclusive voiceprint packet pre-stored in the terminal, setting a similarity threshold value, retaining audio signals of which the similarity reaches the threshold value, and filtering noise signals of which the similarity does not reach the threshold value; and extracting syllable parameters in the reserved audio signal, combining rhythm information, optimizing voice integrity through a feature completion algorithm, and outputting the voice integrity. According to the method, the exclusive voiceprint of the user is taken as a core screening basis, the method is not limited by the noise type, frequency and intensity, all interference signals without target voiceprint can be filtered, the method is adaptive to a complex use scene, and voice distortion caused by traditional noise reduction is avoided.

The invention discloses a robust speech recognition-oriented non-intrusive enhanced speech distortion suppression method. The method comprises the following steps of S1, inputting an original complex frequency spectrum and an enhanced complex frequency spectrum; s2, obtaining a distortion suppression coefficient according to the input in the step S1; and S3, applying the distortion suppression coefficient to a distortion suppression interpolation algorithm to obtain an output correction spectrum. According to the invention, by using the non-intrusive front and back end bridging module, the calculation amount is small, and the method can be compatible with the existing streaming and non-streaming speech enhancement models; the data volume required by enhancement model training is small, and rapid adaptation can be completed on a small amount of labeled data; and the output signal of the enhancement model is not changed, so that the hearing gain of different enhancement algorithms on different enhancement voice aspects is effectively maintained.

The application provides a voice interaction method, device, equipment, medium and program product. The voice interaction method comprises the following steps: determining a first confidence degree of a first original signal and a second confidence degree of a first voice enhanced signal; the first voice enhanced signal is a signal obtained by performing voice enhancement on the first original signal; determining a target signal based on the first confidence degree, the second confidence degree, a second original signal and a second voice enhanced signal; the second voice enhanced signal is a signal obtained by performing voice enhancement on the second original signal; and performing voice interaction with a target device based on the target signal. The application can reduce voice distortion caused by voice enhancement.

The utility model discloses an underwater sound acquisition device capable of avoiding voice distortion, which comprises a shell, a sound wave filtering assembly, a sound wave receiving assembly and a signal transmission part, the shell has a waterproof sealed cavity inside, the sound wave filtering assembly is installed in the waterproof sealed cavity, is used for filtering bass parts formed due to resonance of the sealed space, the sound wave receiving assembly is installed in the waterproof sealed cavity, is used for receiving sound waves filtered through the sound wave filtering assembly, and the signal transmission part is used for realizing signal transmission between the sound wave receiving assembly and external equipment. The underwater sound acquisition device can be installed on a waterproof breathing mask for use to collect human voices of underwater personnel. The sound wave filtering assembly arranged can filter out bass parts formed due to resonance of the sealed space, can avoid voice distortion, the sound wave receiving assembly receives filtered sound waves, and then transmits the sound waves to external equipment through the signal transmission part, so that the sound wave receiving party can receive clear voice information and correctly identify voice content.

The invention relates to the field of industrial control, in particular to an intelligent manufacturing workshop safety interaction control method and system based on a large model, and the method comprises the steps: collecting a sound signal of a workshop environment, calculating the instantaneous energy of a sliding window, constructing an energy concentration ratio, and discriminating local impact noise; introducing adaptive Kalman filtering by taking an energy concentration ratio as a prior factor, tracking a smooth energy change rate, calculating a window length parameter, and adaptively determining a dynamic window length; and after short-time Fourier transform noise reduction, multiplexing or reconstructing window length according to frame similarity, and inputting pure voice into the large voice recognition model to realize safe interaction control. According to the invention, the problems of noise trailing, insufficient frequency resolution and voice distortion easily caused by a traditional fixed window length are solved, the equipment impact noise and the artificial voice instruction can be effectively distinguished, the signal processing precision and the anti-interference capability are improved, and the method is suitable for high-reliability voice safety interaction in a complex industrial environment.