20 results about "A-weighting" patented technology

Embodiments of the present disclosure provide a method for calibrating a sound velocity applied to a multi-layer variable thickness structure, comprising: constructing a planar multi-layer medium stacked structure and determining a sound velocity of each layer of medium material in the planar multi-layer medium stacked structure; establishing a curved multi-layer medium stacked structure, the curved multi-layer medium stacked structure being arranged with a plurality of discrete elements; at the same time, establishing a fluctuation equation for determining a sound pressure value of each of the plurality of discrete elements; establishing a loss function between the sound pressure value of each discrete element calculated by the fluctuation equation and a measured sound pressure value of each discrete element; performing a gradient descent calculation on the sound pressure value of each discrete element based on the loss function, iteratively updating to obtain a sound velocity value being used as a weighting parameter in the plurality of discrete elements until a minimum value of the loss function is obtained, wherein the sound velocity value is an optimal speed value.

The invention relates to the technical field of earphone noise reduction, and discloses an active noise reduction method and device for a head-mounted wireless earphone and the head-mounted wireless earphone. The method comprises the following steps of: performing time division multiplexing sampling through double microphone arrays of left and right acoustic cavities of the earphone to obtain four paths of microphone array sampling data and performing correlation function beam forming calculation to generate sound source direction distribution data; calculating a geometric acoustic path difference of the earphone, and establishing a direction delay parameter table corresponding to each effective sound source direction; driving a multi-channel adaptive filter bank according to the direction delay parameter table, performing directional filtering, and generating a directional inversion signal group; and taking the intensity information in the sound source direction distribution data as a weighting coefficient to perform weighted synthesis on the directional inversion signal group, forming a comprehensive inversion signal, applying a feedforward delay correction value, and outputting a self-adaptive noise reduction signal. According to the method, continuous and stable noise reduction performance can be kept in a complex dynamic multi-sound-source environment, and the auditory experience of a user is remarkably improved.

The invention relates to an intelligent fan abnormal sound distinguishing and detecting method and system based on a support vector machine. The method comprises the steps that a fan sound signal and a fan rotating speed signal are collected; identifying the fan sound signal and intercepting a stable section sound signal; a weighting processing is carried out on the sound signal in the stable section so as to obtain sound features reflecting loudness perception of human ears; the fan abnormal sound feature extraction and screening feature extraction analysis method for the sound signals subjected to A weighting processing comprises the steps that a frequency range is divided into 1 / 12 octave of a finer interval, 15 constant bandwidth energy for inspecting the sound energy condition in a fixed bandwidth and an average frequency spectrum for eliminating noise introduced due to instantaneous change; and inputting the extracted and screened fan abnormal sound feature data into a fan abnormal sound intelligent discrimination model of a support vector machine so as to carry out classification prediction on the normal sound and the abnormal sound of the fan. Intelligent detection of the running state of the fan is achieved.

The invention relates to an intelligent fan abnormal sound distinguishing and detecting method and system based on a time-frequency diagram and deep learning. The method comprises the steps that a fan sound signal and a fan rotating speed signal are collected; identifying the fan sound signal and intercepting a stable section sound signal; a weighting processing is carried out on the sound signal in the stable section so as to obtain sound features reflecting loudness perception of human ears; performing short-time Fourier transform on the sound signal subjected to A weighting processing to obtain a time-frequency graph; and inputting the time-frequency diagram through a ResNet18 deep learning model to carry out fan abnormal sound classification so as to obtain an intelligent detection result of the fan operation state. Intelligent detection of the running state of the fan is achieved.

The present application provides a directional sound pickup method, system, device, and storage medium, relating to the field of sound pickup. The method comprises: performing MVDR beamforming processing on the initial speech signal input by the microphone array, and then extracting the BFCC features and fundamental frequency features of the speech signal. The BFCC features are processed using a pre-trained speech / noise separation DNN to obtain a time-frequency mask. Comb filter parameters are adaptively designed based on the fundamental frequency features, and the initial speech signal is comb filtered. The time-frequency mask is used as a weighting coefficient and multiplied with the comb-filtered speech signal and the initial speech signal, respectively, to estimate the target speech energy and noise energy. The frequency-dependent Bark gain coefficient is calculated on the Bark frequency scale and used to enhance the speech signal in the time-frequency domain. Finally, the enhanced speech signal is converted into a time-domain signal through an inverse BFCC transform to obtain the target enhanced speech pointing in the target direction. The above scheme improves the directional sound pickup effect.

A vehicle abnormal sound identification method, system and device and a computer readable storage medium relate to the field of vehicle abnormal sound detection, and specifically comprise the following steps: determining a weighting coefficient corresponding to each 1 / 6 frequency band based on a weighting coefficient of a 1 / 3 frequency band corresponding to target vehicle noise data; obtaining a target noise characteristic curve and a non-target noise characteristic curve corresponding to each 1 / 6 frequency band; determining a second distance based on a first distance between a target upper limit curve of the target noise characteristic curve and the target noise characteristic curve, the first weighting coefficient and the second weighting coefficient; determining a first upper limit curve of the non-target noise characteristic curve based on the second distance and the non-target noise characteristic curve; and carrying out abnormal sound identification on the target vehicle based on the position relationship between the target noise characteristic curve and the target upper limit curve and the position relationship between the non-target noise characteristic curve and the first upper limit curve. The problem that abnormal sound with low sound power and / or narrow frequency band cannot be effectively identified in the prior art is solved.

The invention discloses a multi-channel active noise control method capable of reducing the calculated amount. The method comprises the following steps: (1) building a multi-channel ANC system, wherein the system comprises M (Mgt; 1) the number of the microphones; (2) measuring a secondary channel transfer function; (3) selecting Q (Qlt; m) microphones, calculating a weighting matrix by using the latest T signals collected by the microphone system, and weighting the signals of the selected Q microphones to obtain a signal of a Q channel for controlling the updating of a filter; (4) updating a control filter; and (5) calculating the output of the control filter, and inputting the output into the secondary sound source for playing. According to the method, the Q microphones are randomly selected in each period of time, and the Q microphone signals are weighted to obtain the update signal of the control filter, so that the calculation amount can be greatly reduced, and the global noise reduction effect can be obtained.

The invention discloses a chassis lining rigidity parallel optimization method and system capable of resisting energy deviation, and relates to the technical field of whole vehicle NVH optimization, and the method comprises the steps: carrying out the modeling of a road noise CAE simulation model, carrying out the analysis of a transmission path, calculating the contribution amount, arranging contribution paths in a descending order, and generating a parameterized lining file according to the linings of the contribution paths; performing dynamic polycondensation on the non-optimized region to form a CMS super-unit, generating a super-unit file, constructing a super-unit hybrid architecture based on the super-unit file and the parameterized lining file, and performing precision verification; and calculating a sound pressure value of a target point in the vehicle, converting the sound pressure value into an A weighting sound pressure level, and constructing an anti-energy offset optimization objective function based on a dynamic weighting superscale function of a superstandard frequency point and an adjacent frequency point correlation penalty function.

The application particularly relates to a target direction estimation method based on weighted deconvolution, which comprises the following steps: receiving far-field signals of a plurality of sound sources by using a sensor array, and constructing array receiving signals according to the far-field signals received by sensors at different positions in the array; configuring an array receiving signal data matrix of a multi-shot condition based on the array receiving signals; determining a covariance matrix corresponding to the array receiving signal data matrix; configuring a weighting vector of a conventional beamformer, and scanning the covariance matrix in different directions in a two-dimensional space by using the conventional beamformer to obtain a corresponding spatial spectrum; calculating the spatial spectrum based on the relationship between a sound source intensity distribution function and a point spread function to obtain a point spread function corresponding to the spatial spectrum; and calling an improved R-L method to perform direction estimation according to the point spread function and the spatial spectrum, and determining the position information of the sound source. The method can be applied to sensor arrays of any shape, and the resolution capability for multiple targets is improved.

The present invention discloses a combined noise reduction method for rock fracture acoustic emission signals based on improved VMD-ITD, belonging to the technical field of signal processing. This noise reduction method first performs VMD decomposition on the acoustic emission signals; from the perspective of signal energy, a method for determining the number of VMD modes based on the component energy ratio is proposed to determine the number of modes K for VMD decomposition; calculate the corresponding mutual information between the K IMF components and the original signal, and use it as a weighting coefficient to perform weighted reconstruction on each IMF component; then perform ITD decomposition on the reconstructed signal and reconstruct again to obtain the noise-reduced signal. The present invention can effectively suppress the noise signals generated during the fracture process of rock samples and extract most of the effective information in the original acoustic emission signals.

The application relates to a sound signal denoising method, device and equipment and a storage medium, which can be used in the technical field of signal processing. The method comprises the following steps: performing variational mode decomposition processing on a to-be-denoised sound signal to obtain a plurality of intrinsic mode components; determining a weighting coefficient of each intrinsic mode component according to a mutual information value between each intrinsic mode component and the to-be-denoised sound signal; performing weighting reconstruction processing on each intrinsic mode component according to the weighting coefficient to obtain a reconstructed signal corresponding to the to-be-denoised sound signal; performing updating processing on wavelet coefficients obtained after multi-scale wavelet decomposition of the reconstructed signal to obtain target wavelet coefficients; and performing inverse wavelet transform processing on the target wavelet coefficients to obtain a denoised sound signal corresponding to the to-be-denoised sound signal. The method can improve the accuracy of sound signal denoising.

The application discloses a beam forming method based on a complex gating recurrent unit, and comprises the following steps: (1) performing complex masking prediction; the number of predicted complex masks corresponds to the number of speakers; (2) performing element multiplication on the complex mask and a complex mixed audio feature to obtain a speaker complex audio feature, performing matrix multiplication on the speaker complex audio feature and a conjugate transpose of the speaker complex audio feature, performing summation along a time dimension after normalization to obtain a complex power spectrum density matrix; (3) the complex power spectrum density matrix corresponds to the audio of each speaker; (4) constructing a complex gating recurrent unit network, inputting a spliced complex matrix feature into the complex gating recurrent unit network, fully utilizing the characteristics of the complex network, and predicting a weighting coefficient of beam forming; after the weighting coefficient of beam forming and the complex mixed audio feature are subjected to matrix multiplication, more accurate speaker complex audio features can be obtained.

Improved methods and / or apparatus for decoding an encoded audio signal in soundfield format for L loudspeakers. The method and / or apparatus can render an Ambisonics format audio signal to 2D loudspeaker setup(s) based on a rendering matrix. The rendering matrix has elements based on loudspeaker positions and wherein the rendering matrix is determined based on weighting at least an element of a first matrix with a weighting factor g=1 / √{square root over (L)}. The first matrix is determined based on positions of the L loudspeakers and at least a virtual position of at least a virtual loudspeaker that is added to the positions of the L loudspeakers.

An audio system utilizes an ambience analysis unit configured to identify early reflections and reverberation present in a first plurality of audio input signals or in processed versions of the first plurality of audio input signals, a weighting unit configured to weigh the early reflections identified in the first plurality of audio input signals or in the processed versions of the first plurality of audio input signals, and the reverberation identified in the first plurality of audio input signals or in the processed versions of the first plurality of audio input signals, and a merging unit configured to assign the weighted early reflections identified in the first plurality of audio input signals or in the processed versions of the first plurality of audio input signals to a first subset of a second plurality of audio output signals.

A method for equalizing an audio frequency signal broadcast in a broadcasting environment. Such a method includes: estimating a frequency profile of a noise signal representing a background noise present in the broadcasting environment, based on, on the one hand, a signal captured by at least one microphone implemented in the broadcasting environment and, on the other hand, the audio frequency signal; determining a desired frequency profile for the broadcast audio frequency signal; determining an acoustic frequency mask representing, for each frequency component, a difference between the frequency profile of the noise signal and the desired frequency profile; and equalizing the audio frequency signal via a weighting of a spectrum of the audio frequency signal by applying a frequency weighting mask that is a function of the acoustic frequency mask, delivering the equalized audio frequency signal.

An audio system comprising: an ambient sound analysis unit configured to identify and / or analyze early reflections and reverberation present in a first plurality of audio input signals or in a processed version of the first plurality of audio input signals; a weighting unit configured to weight the identified early reflections in the first plurality of audio input signals or in the processed version of the first plurality of audio input signals and the identified reverberation in the first plurality of audio input signals or in the processed version of the first plurality of audio input signals; and a merging unit configured to assign the weighted early reflections identified in the first plurality of audio input signals or in the processed version of the first plurality of audio input signals to a first subset of a second plurality of audio output signals and to assign the weighted reverberation identified in the first plurality of audio input signals or in the processed version of the first plurality of audio input signals to a second subset of the second plurality of audio output signals.

The present disclosure provides an audio processing method, an audio processing apparatus and device, and a computer-readable storage medium. The audio processing method includes: acquiring a set of audio data; evaluating each audio data by using a plurality of objective metrics to generate an objective metric vector; acquiring a correlation vector representing correlation between the plurality of objective metrics and a plurality of subjective metrics, and determining a weighting coefficient vector for each audio data based at least on the correlation vector; performing weighted summation on the objective metric vector by using the weighting coefficient vector to obtain an evaluation score; and screening, from the set of audio data, audio data with the evaluation score greater than a first predetermined threshold into a set of target audio data. The set of target audio data is used for establishing training data pair for training neural networks.

The invention discloses a near-field acoustical holography optimization method based on a compressed sensing equivalent source. The near-field acoustical holography optimization method comprises the following steps of 1) performing equivalent processing on a space sound field; step 2), selecting the position and size of a holographic measurement surface in a potential sound source near field, performing equidistant grid division on the holographic surface, and determining a sampling strategy; 3) selecting a certain number of measurement points on the holographic measurement surface in a random manner, constructing a near-field acoustic holographic model based on the compressed sensing equivalent source, and solving an initial sparse solution of the source intensity of the equivalent source by using L1 regularization according to the sound pressure at the measurement points randomly selected in the holographic surface; according to the method, the sparsity of an equivalent source intensity solution can be further improved, the influence of a pseudo source is effectively restrained, the convergence speed of iterative calculation can be increased, and the calculation efficiency is improved.

The invention discloses a real-time audio analysis and diagnosis method for the inspection process of a continuous annealing furnace. The method comprises the following steps: S1, waiting for an audio file address of rotary equipment to be transmitted into a text file of an audio file storage path; s2, reading an audio file address; s3, acquiring audio file information; s4, performing A weighting on the audio data in the audio file; s5, calculating the total sound pressure level decibel of the audio data weighted by A in each time period; s6, carrying out short-time Fourier time-frequency analysis on the audio data weighted by A to obtain amplitudes at all moments and frequencies; s7, calculating 1 / 3 octave of the audio data weighted by A, and obtaining a sound pressure level mean square value in each octave; s8, storing analysis and diagnosis results of the steps S5-S7; and S9, emptying the text file for storing the audio file path. According to the invention, the sound data is analyzed and diagnosed in real time, the diagnosis result is stored in real time, and the real-time performance and automation of furnace area equipment monitoring are realized.

The invention discloses a sound level meter A weighting filter design method based on IWOA and a measuring device. The method comprises the steps that a target sub-filter needing to be optimized in a sound level meter A weighting filter is determined; performing digital modeling on the target sub-filter to obtain a digitized filter model, establishing constraint conditions for the digitized filter model, and constructing a target optimization function to obtain an optimization problem model of the target sub-filter; and performing parameter optimization on the optimization problem model by adopting an improved whale optimization algorithm IWOA to obtain optimal parameters in the digitized filter model, thereby obtaining an optimized target sub-filter. The invention aims to reduce the error of the A weighting filter in the sound level meter, ensure the overall measurement accuracy of the sound level meter, reduce the influence of measurement uncertainty, and enable the sound level meter to make accurate response to noise of various frequencies in various complex occasions.