15 results about "Sound separation" patented technology

The present invention addresses the problem of providing an "audio signal processing device" that achieves a good sense of localization with respect to a sound having a narrow frequency band. [Solution] The present invention is provided with signal processing units provided so as to correspond to respective channels of audio signals of a plurality of channels, and each signal processing unit has: a target sound separation unit (411) that separates a target sound signal indicating a target sound, which is a sound to be subjected to localization enhancement, from a source sound signal, which is an audio signal of the corresponding channel; a sense-of-localization-enhanced sound generation unit that generates a sense-of-localization-enhanced sound signal in which the frequency of the target sound signal separated by the target sound separation unit (411) is multiplied by k (k is an integer of 2 or more); and a synthesis unit that synthesizes and outputs the localization-enhanced sound signal generated by the localization-enhanced sound generation unit and the source sound signal.

The invention belongs to the field of sound separation processing, and discloses an individual perception multi-bird sound separation method and system based on identity embedding, and the method comprises the steps: S1, carrying out the preprocessing of an audio segment containing bird sound, obtaining a preprocessed audio segment, and obtaining a time domain waveform corresponding to the audio segment; s2, carrying out separation processing on the time domain waveform to obtain a separated individual audio waveform; and S3, based on the separated individual audio waveform, carrying out classification identification on the bird sound. According to the method, under the conditions that voiceprint differences of individuals of the same type of birds are extremely small and sound sources are highly overlapped, stable individual consistency and separation precision can still be kept. According to the method, the technical bottleneck of identity drift and pseudo separation of a traditional model in long-time sequence monitoring is solved, refinement and automation of individual-level ecological acoustic analysis are also realized, and a feasible technical path is provided for field long-term ecological monitoring and acoustic big data processing.

The application discloses a mechanical arm deception attack side channel detection method based on acoustic characteristics and belongs to the technical field of deep learning, which comprises the following steps: constructing a training recognition model; receiving the collected target audio, performing a sound processing step operation to extract features, performing a training recognition step operation to output predicted motion data; comparing the predicted motion data with the obtained corresponding real-time motion data, and evaluating whether the difference between each pair of parameters exceeds a preset threshold value. The application uses sound separation technology to separate mixed multi-axis sound into different individual axis sound, constructs an acoustic motion information recognition model of each motion joint through a collaborative physical and data-driven method, identifies the target sound according to the recognition model, obtains the predicted motion information of each axis, compares the predicted motion information with the collected real-time motion command, combines a threshold value to determine whether a deception attack is received, and realizes mechanical arm deception attack side channel detection.

The application discloses a sound isolation method and device based on deep learning and a storage medium. The method comprises the following steps: obtaining an audio file used for constructing a DeepAudioSep model and preprocessing the audio file used for constructing the DeepAudioSep model; constructing the DeepAudioSep model and training the DeepAudioSep model, wherein the DeepAudioSep model comprises one mixed source input and ten isolated source outputs; and performing sound separation through the DeepAudioSep model. The application introduces the data driving and deep learning idea into sound separation and noise isolation processing, improves the sound separation and noise isolation processing capability in the environmental monitoring field, and therefore has wide noise processing prospects and practical value.

We provide an "audio signal processing device" that achieves good localization for sounds with a narrow frequency band. [Solution] The system has a signal processing unit corresponding to each channel of a multi-channel audio signal. Each signal processing unit includes a target sound separation unit 411 that separates a target sound signal, which is the sound to be targeted for localization enhancement, from a source sound signal, which is the audio signal of the corresponding channel; a localization enhancement sound generation unit that generates a localization enhancement sound signal by multiplying the frequency of the target sound signal separated by the target sound separation unit 411 by k (where k is an integer of 2 or more); and a synthesis unit that synthesizes the localization enhancement sound signal generated by the localization enhancement sound generation unit and the source sound signal and outputs the result.

This invention discloses a multi-target sleep monitoring method and device based on snoring separation. The method includes: constructing independent respiratory wave time-stamped sequences corresponding to each monitored object based on the radio frequency signal corresponding to the target monitoring area; constructing a mixed snoring time-stamped sequence corresponding to all monitored objects based on the mixed environmental audio signal corresponding to the target monitoring area; performing cross-modal correlation matching between the mixed snoring time-stamped sequence and each independent respiratory wave time-stamped sequence to determine the independent snoring time-stamped sequence corresponding to each monitored object; and performing sleep monitoring analysis on the corresponding monitored objects based on the independent respiratory wave time-stamped sequence and / or the independent snoring time-stamped sequence of the same monitored object to obtain the sleep monitoring results corresponding to each monitored object. This achieves accurate sleep monitoring analysis of multiple monitored objects in the same space, resulting in precise sleep monitoring analysis results.

According to an aspect of the present disclosure there is provided a method for adversarial training of a separator (30) for universal sound separation of an audio mixture m of arbitrary sound sources Sk=1, . . . ,K, the method comprising: training a context-based discriminator (34) configured to provide a context-based loss cue based on a consideration of an input set of separated sound sources; and training the separator (30) to minimize a loss based on the context-based loss cue provided by the context-based discriminator (34); wherein training the context-based discriminator (34) comprises maximizing a loss based on a set of ground-truth sound sources and a fake set of separated sound sources, wherein the fake set of separated sound sources is sorted to match an order of the set of ground-truth sound sources, and wherein the fake set of separated sound sources comprises sources corresponding to separated sound sources estimated by the separator (30) and further comprises one or more ground-truth sound sources of the set of ground-truth sound sources.

The invention discloses a dry and wet sound separation method and a related device, and relates to the technical field of audio processing, the dry and wet sound state is introduced, and the signal state is determined, so that the separation strategy better fits the dynamic change of the signal. On the basis, when dry and wet sound separation is realized, a spatio-temporal information decoupling technology is adopted, spectrum information which changes rapidly and relatively stable spatial information are respectively processed and cooperatively utilized, interference of spatial aliasing on spectrum separation is avoided, a separation result is verified and optimized through spatial correlation, the problem that stereo spatial information is not fully utilized is solved, and the separation efficiency is improved. Therefore, the spatial information of stereophonic sound is fully mined, and the overall separation effect is improved.

The invention provides a vehicle external sound processing method and system and a storage device. The method comprises the steps that original environment sound signals in multiple directions outside a vehicle are collected; based on a target sound pointing instruction, performing beam forming processing on the original environment sound signal, and extracting a first sound signal in a specific direction; performing noise suppression and target sound separation processing on the first sound signal to obtain a second sound signal; and performing high-fidelity audio reconstruction and amplification on the second sound signal to obtain an audio signal of the target sound, and outputting the audio signal to an in-vehicle loudspeaker. According to the method provided by the invention, directional screening and noise reduction of the environment sound outside the vehicle and high-fidelity playback in the cabin can be realized.

The invention provides a stereophonic sound processing method and device, and relates to the technical field of sound processing, and the method comprises the steps: carrying out the dry and wet sound separation processing of a stereophonic sound audio signal comprising a left sound channel signal and a right sound channel signal, and obtaining a dry sound, a left channel wet sound, and a right channel wet sound; wherein the left channel wet sound is a signal component irrelevant to a right channel signal in the left channel signal, the right channel wet sound is a signal component irrelevant to the left channel signal in the right channel signal, and the dry sound is a signal component with the same amplitude and phase in the left channel signal and the right channel signal; performing filtering processing on the dry sound, the left channel wet sound and the right channel wet sound by using a preset first control filter, a preset second control filter and a preset third control filter respectively to obtain three paths of corresponding filtering signals; and superposing the three paths of filtering signals to generate a multi-channel output signal for driving the loudspeaker array.

The described aspects and implementations enable privacy-respecting detection, separation, and localization of sounds in vehicle environments. The techniques include obtaining, using audio detector(s) of a vehicle, a sound recording that includes a plurality of elemental sounds (ESs) in a driving environment of the vehicle, and processing, using a sound separation model, the sound recording to separate individual ESs of the plurality of ESs. The techniques further include identifying a content of individual ESs and causing a driving path of the vehicle to be modified in view of the identified content of the individual ESs. Further techniques include rendering speech imperceptibly by redacting temporal portions of the speech, using sound recognition models to identify and discard recordings of speech, and driving at speeds that exceed threshold speeds at which speech becomes imperceptible from noise masking.

The invention discloses an audio reverberation space clue parameter estimation method and device and a storage medium, and relates to the technical field of audio processing. The method comprises the following steps: acquiring a target wet sound signal, and carrying out dry and wet sound separation on the target wet sound signal to obtain a dry sound signal and a pure reverberation signal; obtaining parameter estimation rules corresponding to a plurality of target audio space cue parameters used for representing reverberation space characteristics; the parameter estimation rule is pre-constructed based on the correlation between the signal statistical characteristic of the audio signal and the audio space clue parameter; and according to the parameter estimation rule, analyzing the target wet sound signal, the dry sound signal and the pure reverberation signal to obtain a target audio space clue parameter of the target wet sound signal. Automatic audio reverberation space clue parameter estimation can be realized, the audio space clue parameter estimation efficiency and accuracy are improved, and the method is suitable for linear and nonlinear reverberation effects.

Provided is an acoustic signal analysis system and the like that can accurately separate a sound into its individual components even if the components of the constituent units overlap in time. [Solution] An approximation calculation unit 31 performs nonnegative matrix factorization on a spectrogram matrix obtained by short-time Fourier transform of an acoustic signal of a speech sound to estimate a basis matrix indicating a spectral pattern and an activation matrix indicating time changes in signal strength. A normalization unit 32 normalizes the basis matrix using the Euclidean norm of the basis matrix and scales the activation matrix using the Euclidean norm. A separation unit 33 separates the acoustic signal into basis components based on the normalized basis matrix and the scaled activation matrix.