145 results about "Speech denoising" patented technology

The invention relates to the field of intelligent voice analysis, in particular to a fuzzy instruction analysis method and system based on dual-channel noise reduction and a dynamic semantic map. The method comprises the following steps: (1) noise reduction voice recognition: combining dual-channel voice noise reduction and voice recognition, extracting a target voice signal in a high-noise environment, completing voice-to-text conversion, and outputting text data corresponding to the target voice signal; (2) multi-modal fusion: dynamically adjusting the weights of the voice data, the visual data and the sensor data based on real-time environment perception; (3) fuzzy instruction analysis: receiving the text data output in the step (1), and combining the weights of the optimized voice data, visual data and sensor data output in the step (2) to realize semantic map updating; a target device is positioned by using the dynamically updated semantic map in combination with device layout, environmental data and user instruction history, and a fuzzy instruction is analyzed. According to the method provided by the invention, the speech recognition precision and the fuzzy instruction analysis capability can be improved, and the operation flexibility is remarkably improved.

The invention relates to the technical field of dual-mode interphones, and discloses an AI-based dual-mode interphone satellite communication noise reduction method and system, and the method comprises the steps: carrying out the orbit equation solving of satellite ephemeris data, and obtaining a satellite elevation angle and a channel fading parameter; receiving a first voice signal through a dual-mode interphone, and identifying a modulation mode according to the first voice signal; calculating a first noise reduction mask according to the channel fading parameter and the modulation mode; compressing the standard noise reduction neural network to form a lightweight noise reduction model; and starting an FM spectrum subtraction branch or a TETRA filtering branch in the lightweight noise reduction model according to the modulation mode, and performing frequency domain processing on the first noise reduction mask and the first voice signal to obtain a second voice signal, thereby providing stable and reliable real-time voice noise reduction capability for the portable dual-mode interphone in a dynamic satellite environment.

The invention relates to the technical field of artificial intelligence, and discloses a voice noise reduction method and device based on a vibration sensor, equipment and a medium, and the method comprises the steps: synchronously collecting a mixed signal and a reference signal of the vibration sensor, generating linear echo estimation, and calculating an initial residual error, extracting time-frequency characteristics of the initial residual error and the sensor reference signal to obtain nonlinear echo estimation; adjusting the initial residual error according to a double-talk state decision function and the nonlinear echo estimation to obtain a target residual error; and processing the target residual error through a rear Kalman filtering noise reduction module, and outputting pure near-end voice. The method has the advantages that noise in voice signals is effectively reduced, the voice recognition precision and definition are improved, the advantages of adaptive filtering and deep learning are combined, the method has good adaptability to various environment characteristics, and the response capacity and robustness to dynamic environment changes are improved.

The invention discloses a two-stage voice noise reduction method and device based on enhanced spectral subtraction and Kalman filtering, and a storage medium. The method comprises the following steps: performing preliminary noise reduction on a voice signal in a frequency domain through enhanced spectral subtraction; speech signal state space model parameters are identified based on a linear predictive coding algorithm, and secondary noise reduction is performed on the sound signals in a time domain by using a Kalman filtering algorithm; and repeatedly carrying out multiple parameter identification and secondary noise reduction processes on the frame-by-frame signals, and then outputting noise-reduced voice. According to the method, the environmental noise can be effectively filtered in a complex noise environment, and the voice signal quality is improved. The method is suitable for single-microphone noise reduction of the artificial cochlea, efficient noise reduction can be achieved in the artificial cochlea with limited computing resources, and the sound perception ability of a wearer is improved.

The invention relates to the technical field of voice noise reduction, in particular to a data center scene-oriented voice noise reduction method and system, a terminal and a storage medium, and the method comprises the steps: carrying out the STFT time-frequency transformation of a to-be-denoised voice, and obtaining a complex frequency spectrum of the to-be-denoised voice; adopting a multi-scale CNN convolutional neural network to perform feature extraction on the complex frequency spectrum; performing time sequence modeling on the extracted features by using a bidirectional long short-term memory (LSTM) network; voice features in the features after time sequence modeling are separated; carrying out reverberation suppression on the separated voice features; and inverse STFT reconstruction is carried out on the voice features after reverberation suppression, and the voice after noise reduction is obtained. The method is used for solving the problem of time-frequency-space three-dimensional coupling of the noise of the data center.

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 tablet computer real-time speech recognition and translation system based on side cloud collaboration, which relates to the technical field of speech processing, and comprises a speech noise reduction module for performing multi-stage enhancement by adopting beam forming and a deep residual network, performing multi-channel feature fusion in combination with adaptive noise estimation and an attention mechanism, and performing speech recognition and translation. Obtaining clean voice data after signal-to-noise ratio optimization; the language recognition module inputs the clean voice data into a language recognition network, extracts a voice feature vector, performs language recognition through a language clustering model and generates a language tag; and the translation module is used for carrying out semantic optimization through semantic understanding and context modeling according to the preliminary recognition text, and carrying out translation processing by utilizing a neural network translation model to generate a translated text. According to the invention, the technical effect of effectively improving the signal-to-noise ratio of the voice signal in a complex acoustic environment is realized.

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.

A voice noise reduction method, system, device and medium based on a differential beam former, the method is based on a theoretical beam pattern of the differential beam former, and uses a Chebyshev polynomial to derive a relationship among a main lobe width, a side lobe amplitude and a Chebyshev polynomial order, so as to reduce noise of the main lobe width, the side lobe amplitude and the Chebyshev polynomial order. Further constructing an ideal beam pattern capable of accurately controlling the width of a main lobe and the amplitude of a side lobe; based on the constructed ideal beam pattern, constructing different beam formers by adopting a zero point constraint method, a diagonal loading mode and a combined delay summation beam former; the beam formers can accurately control the width of a main lobe and the amplitude of a side lobe of a beam, can carry out direction adjustment at any angle under the condition of no beam pattern distortion, can be suitable for any array of microphone arrays, are robust to self-noise of microphones, can effectively carry out voice noise reduction, and have good applicability and robustness.

The invention relates to a voice signal filtering and noise reduction method based on a Mama generative adversarial network, and the method comprises the following steps: designing a noise synthesis strategy for different application scenes, collecting real environment noise to construct a mixed noise library, and constructing a voice data set with noise and a voice data set without noise, and the voice data set and the voice data set do not need to be matched; constructing a Mama-based generative adversarial network, and realizing end-to-end conversion from the noisy voice to a time sequence filtering result; the construction of the model comprises the steps of designing a generator network, designing a discriminator network and optimizing a loss function. According to the invention, the robustness and generalization ability of voice signal noise reduction can be improved.

The present invention discloses a speech enhancement method that combines hearing loss compensation and speech noise reduction, comprising: extending and embedding a hearing loss map along the frequency axis to obtain a hearing loss spectrum, and superimposing the hearing loss spectrum with the complex spectrum features of the noisy training speech; constructing a metric generative adversarial network model based on frequency-time convolution recursion, the model's main structure comprising a compensation generator and a metric discriminator; alternately training the compensation generator and the metric discriminator to optimize the metric generative adversarial network model; superimposing the complex spectrum features of the test speech with the hearing loss spectrum and inputting them into the trained compensation generator, and reconstructing an enhanced speech waveform of the test speech based on the output of the compensation generator. The present invention uses a metric generative adversarial network to simultaneously achieve noise reduction and hearing loss compensation for a specific audiogram, capable of stably and effectively improving the effectiveness of hearing loss compensation in noisy environments. The method is ingenious and novel, with promising application prospects.

A noise reduction method and device, a terminal sound transmission method and device, and a server noise reduction processing method and device, the noise reduction device comprising: a sound signal acquisition module for generating a sound signal according to collected sound information, the sound information comprising a voice and a first environment sound; the memory is suitable for storing a voice noise reduction program, the voice noise reduction program is generated according to an environment sound signal, and the environment sound signal is generated according to a second environment sound; and the processor is suitable for executing the voice noise reduction program to carry out noise reduction processing on the sound signal. According to the invention, the voice noise reduction program suitable for the current scene can be obtained, and sound signal noise reduction requirements in different scenes can be met.

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.

An embodiment of this application provides a speech processing method and apparatus, and an apparatus for speech processing, applied to a terminal device, where the terminal device is equipped with at least two microphones. The method includes: performing summation on signals received by the at least two microphones to obtain a first signal, and performing subtraction on the signals received by the at least two microphones to obtain a second signal; performing blind separation on the first signal and the second signal to obtain a speech signal and a noise signal; and performing adaptive noise cancellation on the speech signal based on the noise signal to obtain a target speech signal. The embodiments of this application can optimize a speech denoising effect, and further improve the speech recognition accuracy of the terminal device in a complex and changeable environment with large noise or strong interference.

The present disclosure provides a method for training a noise reduction model, a speech noise reduction method, an apparatus and an electronic device. The method comprises: obtaining source noisy speech data, and performing reverberation processing on the source noisy speech data to generate corresponding training samples, wherein the training samples comprise a plurality of samples, and each sample comprises multi-channel reverberation noisy speech data; training a to-be-trained speech noise reduction model based on the multi-channel reverberation noisy speech data included in the training samples to obtain a trained first speech noise reduction model. In the present disclosure, the human ear has a good listening experience for the noise reduction speech output by the trained speech noise reduction model, improves the propagation accuracy of the information carried in the speech data, reduces the influence of noise on the propagation of the information carried in the speech data, improves the robustness of the speech noise reduction model, strengthens the applicability and practicality of the speech noise reduction model, and optimizes the training method and training effect of the speech noise reduction model.

The present application relates to the field of speech processing technology and discloses a two-stage speech denoising method, apparatus, computer device, computer-readable storage medium and computer program product based on amplitude spectrum and complex spectrum. The method comprises performing a preprocessing operation on the acquired original speech signal and dividing it into a number of time frames according to a preset interval; determining the amplitude, phase information and complex spectrum of each different frequency component; using the amplitude of each different frequency component to perform noise estimation, performing preliminary noise suppression on the original noisy speech to obtain a preliminary denoised amplitude spectrum; based on the preliminary denoised amplitude spectrum, combined with the phase information, converting it into a preliminary denoised complex spectrum; using the complex spectrum of the original noisy speech and the preliminary denoised complex spectrum to perform noise estimation, and converting it into a complex spectrum of the target speech signal. The present application has the effect of improving the denoising accuracy when processing noisy speech with a low signal-to-noise ratio or a mixture of multiple types of noise.

The invention discloses a voice noise reduction method based on an improved threshold function, and belongs to the technical field of signal processing. The voice noise reduction method based on the improved threshold function comprises the following steps: performing wavelet transform on an obtained noisy voice signal to obtain an initial wavelet coefficient corresponding to each decomposition level of the noisy voice signal; determining a first parameter and a second parameter based on the initial wavelet coefficient and a global threshold corresponding to the noisy voice signal, and generating an improved threshold function corresponding to each decomposition level based on the first parameter and the second parameter; based on the improved threshold function, processing the initial wavelet coefficient corresponding to each decomposition hierarchy to obtain a target wavelet coefficient corresponding to each decomposition hierarchy; and performing wavelet inverse transformation on the target wavelet coefficient corresponding to each decomposition level to obtain a target voice signal corresponding to the noisy voice signal. According to the voice noise reduction method based on the improved threshold function, the reconstruction precision is improved, and the noise reduction effect is good.

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 present application relates to a method for speech noise reduction with controllable time delay, comprising the following steps: framing the noisy speech, transforming the noisy speech into a complex spectrum of the noisy speech in the time-frequency domain; determining a gain function based on the complex spectrum of the noisy speech; the gain function being a real number or a complex number; determining a time domain filter based on the gain function, wherein the order of the time domain filter is set according to the time delay requirement; inputting the noisy speech into the time domain filter for noise reduction processing that meets the time delay requirement to obtain pure speech. The method proposed in the embodiment of the present application can achieve advanced speech noise reduction performance under low-latency conditions, reduce computational complexity, and improve robustness.

The present invention belongs to the field of speech noise reduction processing. The present invention discloses an intelligent communication device speech noise reduction method and system based on artificial intelligence; including an intelligent communication device, deploying a microphone array on the intelligent communication device to collect real-time noisy speech data of users; inputting the collected noisy speech data into the edge layer, and obtaining noise-reduced speech data through real-time processing; pre-constructing a dialect speech database, adding a scene evolution layer, a phonetic variation layer, and a noise mixing layer to the conditional adversarial network cGAN to expand the dialect speech database, forming data samples, and performing three-dimensional annotation on each speech data in the data samples; designing an input, output, and multi-task learning mechanism based on an end-to-end speech enhancement model, and using the data samples for pre-training, setting a shared encoder in the multi-task learning mechanism for joint tasks R1, R2, and R3 to achieve speech noise reduction of the intelligent communication device.

The embodiment of the invention relates to the technical field of vehicles, and provides a vehicle-mounted communication method and device and electronic equipment. The method comprises the following steps: acquiring a multi-modal signal of a first user, wherein the multi-modal signal comprises a first sound signal and an image signal of the first user; determining text information corresponding to the multi-modal signal based on the multi-modal signal of the first user; determining a second sound signal corresponding to the text information based on the text information; and outputting the second sound signal to the second user. Therefore, according to the technical scheme provided by the embodiment of the invention, the original voice is not output to the second user after noise reduction, but the new voice, namely the second sound signal, is synthesized to the second user, so that the interference of noise can be avoided, and the sound quality of the vehicle-mounted call voice is ensured.

The present application provides a training method for a voice noise reduction model and a voice enhancement method. The voice noise reduction model includes: a first enhancement module and a second enhancement module. The first enhancement module is used to perform noise reduction processing on the input spectrum and output the spectrum; the second enhancement module is used to perform noise reduction processing on the input spectrum and output a complex mask. The processing order of the first enhancement module and the second enhancement module is determined according to the signal-to-noise ratio of the sound channel. Among them, when the signal-to-noise ratio of the sound channel is less than a preset value, the first enhancement module is first used for processing to restore the voice harmonics, and then the second enhancement module is used for processing to enhance the noise reduction performance.

The present application provides a method and device for game team voice noise reduction based on multi-channel signal processing. The method includes: obtaining a multi-channel audio signal, and synchronously sampling and digitally processing the multi-channel audio signal; dividing the processed multi-channel audio signal into frames, and determining the corresponding energy value according to the decibel value of each frame of the audio signal; filtering or attenuating the audio signal with an energy value lower than the energy threshold, and locating the sound source according to the multi-channel audio signal after preliminary noise reduction; performing beamforming and multi-channel noise reduction processing on the multi-channel audio signal after preliminary noise reduction according to the target voice direction information; performing voice activation detection and gain control on the voice signal obtained after beamforming and multi-channel noise reduction processing, and outputting the optimized voice signal to the game communication module. The present application can improve the noise suppression effect in complex noise environments, improve the real-time performance and stability of voice, and improve voice quality.

The present invention discloses a method, device, equipment and computer-readable storage medium for speech noise reduction. The speech noise reduction method includes: acquiring first speech data collected by a microphone and second speech data collected by a bone conduction sensor; inputting the speech data in a first frequency band in the first speech data and the speech data in a second frequency band in the second speech data into a speech fusion noise reduction network for prediction to obtain target noise-reduced speech data; wherein the first frequency band is greater than the second frequency band; the speech fusion noise reduction network is pre-trained by using microphone noisy speech data and bone conduction noisy speech data as input data and the microphone clean speech data corresponding to the microphone noisy speech data as training labels. The speech noise reduction solution of the present invention improves the speech noise reduction effect.

The present invention discloses a speech noise reduction method, apparatus, device, and computer-readable storage medium. The method comprises: obtaining preliminary noise reduction data obtained by performing preliminary noise reduction on original speech data collected by a microphone; determining correction standard data corresponding to each first frequency point based on a speech model, wherein the speech model is spectral data determined based on a Gaussian mixture model; comparing the frequency point data of each first frequency point in the preliminary noise reduction data with the correction range defined by the correction standard data of the corresponding frequency point, correcting the frequency point data that exceeds the corresponding correction range to fall within the corresponding correction range, obtaining corrected noise reduction data, and using the corrected noise reduction data as the noise reduction result of the original speech data. The present invention provides a speech noise reduction scheme that further improves the speech noise reduction effect by further correcting the noise reduction speech data after preliminary noise reduction using a speech noise reduction algorithm.

The present application provides a method, device and medium for reducing the noise of game team voice based on a dynamic threshold mechanism. The method includes: dividing the audio signal into multiple time frames, converting the amplitude or decibel value of the time frame to obtain an energy value; comparing the energy value with the initial threshold, if the energy value is lower than the initial threshold, marking it as a noise frame or a low-priority frame, if the energy value is higher than the initial threshold, retaining it as a voice frame to be further processed; comparing the energy value with the dynamic threshold, if the energy value is lower than the dynamic threshold, filtering the corresponding time frame as a noise frame, if the energy value is higher than the dynamic threshold, retaining the corresponding time frame as a valid voice frame; encoding the time frame retained after filtering by the dynamic threshold to generate the final game team voice signal. The present application can reduce the phenomenon of missed or misjudgment of noise, realize dynamic and accurate filtering of noise under different environmental noise conditions, and improve user experience.

The present application provides a method, apparatus, electronic device, and storage medium for training a voice noise reduction model; the voice noise reduction model includes: a noise processing layer, a pronunciation difference processing layer, and a content difference processing layer, and the method includes: through the noise processing layer, performing noise reduction processing on a voice sample to obtain a target voice sample; through the pronunciation difference processing layer, predicting a pronunciation score for the target voice sample to obtain a pronunciation prediction result, and the pronunciation prediction result is used to indicate the pronunciation similarity between the target voice sample and the reference pronunciation corresponding to the voice sample; through the content difference processing layer, determining the content difference between the content of the target voice sample and the content of the voice sample; based on the pronunciation prediction result and the content difference, updating the model parameters of the voice noise reduction model to obtain a trained voice noise reduction model; through the present application, the noise reduction accuracy of the voice noise reduction model can be improved.