21 results about "Linear prediction coding" patented technology

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention belongs to the field of artificial intelligence and non-contact measurement, and discloses a lung function measurement and evaluation method based on a smartphone microphone. The lung function measurement based on the smartphone microphone comprises the following two parts. An expiration audio signal is collected through a microphone of the smart phone under the condition that the smart phone does not need to wear any external equipment. A CNN-LSTM cascade structure is constructed to convert an audio signal into approximate flow through a nonlinear relation between a deep learning analog audio signal and a flow volume curve, and a linear predictive coding (LPC) feature is used for improving an index effect. The invention provides a deep learning model for generating a flow volume curve by combining an audio signal with a signal linear predictive coding feature. According to the method, the comprehensiveness of lung function measurement based on the smart phone is improved, and lung function measurement can be conveniently and effectively carried out through the smart phone in a family environment.

Methods and systems for advanced stereo processing of an audio signal are disclosed. The methods and systems include selecting a coding mode of either transform coding or linear predictive coding and performing advanced stereo processing when in the selected coding mode. Both encoding and decoding operations are provided.

Methods and systems for advanced stereo processing of an audio signal are disclosed. The methods and systems include selecting a coding mode of either transform coding or linear predictive coding and performing advanced stereo processing when in the selected coding mode. Both encoding and decoding operations are provided.

The invention provides a voice packet loss hiding method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the framing processing of a received voice signal, and obtaining a current frame signal and a historical reference frame signal; based on the current frame signal, calculating an LPC coefficient through linear predictive coding LPC analysis; performing echo detection on the current frame signal and the historical reference frame signal, and judging whether an echo exists in the current frame signal; if it is judged that the echo exists, echo cancellation processing is started, and an LPC coefficient is updated based on an echo cancellation result to obtain an updated LPC coefficient; the updated LPC coefficient is used for frame reconstruction when subsequent voice packet loss occurs. According to the invention, through real-time echo detection and elimination of echo components in the reconstructed voice, the core problem of echo generation due to limitation of an LPC model in a transient phase is solved in a targeted manner, so that the reconstructed voice is purer and more natural, and the voice quality is remarkably improved.

The present invention relates to a method for synthesising a speech signal, the method comprising the following steps: - obtaining a coded stream coded with mixed-excitation linear prediction coding; - extracting the parameters of the coded stream; - applying a neural network to extracted parameters in order to obtain a set of output parameters comprising a first set of gains and first spectral envelope parameters, wherein the gain and the spectral envelope are the gain and the spectral envelope of the speech signal to be synthesised in the 4 kHz - 8 kHz frequency band; - merging parameters derived from the set of output parameters and the extracted parameters of the coded stream; and - applying a mixed-excitation linear prediction synthesis technique to the parameters obtained after the merging step in order to obtain the speech signal to be synthesised.

Methods, an encoder and a decoder are configured for transition between frames with different internal sampling rates. Linear predictive (LP) filter parameters are converted from a sampling rate S1 to a sampling rate S2. A power spectrum of a LP synthesis filter is computed, at the sampling rate S1, using the LP filter parameters. The power spectrum of the LP synthesis filter is modified to convert it from the sampling rate S1 to the sampling rate S2. The modified power spectrum of the LP synthesis filter is inverse transformed to determine autocorrelations of the LP synthesis filter at the sampling rate S2. The autocorrelations are used to compute the LP filter parameters at the sampling rate S2.

Methods and systems for advanced stereo processing of an audio signal are disclosed. The methods and systems include selecting a coding mode of either transform coding or linear predictive coding and performing advanced stereo processing when in the selected coding mode. Both encoding and decoding operations are provided.

Methods and systems for advanced stereo processing of an audio signal are disclosed. The methods and systems include selecting a coding mode of either transform coding or linear predictive coding and performing advanced stereo processing when in the selected coding mode. Both encoding and decoding operations are provided.

Methods and systems for advanced stereo processing of an audio signal are disclosed. The methods and systems include selecting a coding mode of either transform coding or linear predictive coding and performing advanced stereo processing when in the selected coding mode. Both encoding and decoding operations are provided.

The present invention relates to a method for synthesising a voice signal, the method being computer-implemented and comprising the steps of: - obtaining a stream coded with mixed-excitation linear prediction coding, - extracting the parameters of the coded stream, - applying at least one neural network to the extracted parameters to obtain the probability density of the linear prediction residual signal associated with the coded stream, - generating an estimate of the linear prediction residual signal estimated from the obtained probability density, so as to obtain an excitation signal, and - synthesising the speech based on the excitation signal.

A linear prediction coding (LPC) parameter coding method is provided. The method includes: determining a reference LPC parameter from a plurality of LPC parameters, performing direct coding on the reference LPC parameter, and performing reference coding on a non-reference LPC parameter based on the determined LPC parameter. The method includes: obtaining a direct coding result of the reference LPC parameter and determining a residual coding result of the non-reference LPC parameter.

The present invention relates to a method for synthesising a speech signal, the method comprising the steps of: - obtaining a coded stream coded with mixed-excitation linear prediction coding; - extracting the parameters of the coded stream; - applying a neural network to extracted parameters in order to obtain a set of output parameters comprising a first set of gains and first spectral envelope parameters, wherein the gain and the spectral envelope are the gain and the spectral envelope of the speech signal to be synthesised in the 4 kHz - 8 kHz frequency band; - generating a first signal from parameters derived from the output parameters; - applying a mixed-excitation linear prediction synthesis technique to the extracted parameters in order to obtain a second signal; and - merging the signals to obtain the speech signal to be synthesised.

The invention relates to the technical field of voiceprint identification, and particularly discloses a popular music singer style identification method and system based on voiceprint recognition, and the method comprises the steps: carrying out the sound source separation of a popular music audio file, and carrying out the end point detection segmentation of a separated pure human voice track, and obtaining continuous sound production segments; performing linear predictive coding analysis on the continuous sounding segments to obtain sound channel response parameters; performing recursive feature decoupling on the sound channel response parameters to obtain a sound channel cepstrum feature sequence; mapping the sound channel cepstrum feature sequence to a high-dimensional space, and performing chaos invariant estimation on a phase space trajectory obtained by mapping to obtain a double-parameter chaos feature; performing similarity comparison on the double-parameter chaos features to identify styles and genres of singers in the popular music audio file; according to the invention, the efficiency of identifying the style of the popular music singer based on voiceprint recognition can be improved.

According to a method for analyzing the state of an operating device for electricity, a detection voltage is applied to the operating device. Subsequently, a measurement signal is acquired at a connection point (AS) of the operating device. A transmission parameter is determined from the measurement signal, and the transmission parameter characterizes the signal transmission from the location of partial discharge in the operating device to the connection point (AS). Based on the transmission parameter, at least one characteristic parameter of the partial discharge is determined. The determination of the transmission parameter includes processing the measurement signal according to a method for linear predictive coding.

The application relates to the technical field of network security, and provides a blockchain network attack detection method, device, equipment, medium and program product, which comprises the following steps: acquiring network access behaviors between blockchain nodes, extracting network access features from the network access behaviors and constructing a network access feature set; determining linear predictive coding spectrum features of the network access feature set according to preset network access targets and time slices; inputting the linear predictive coding spectrum features into a time sequence prediction network, taking an output state of the time sequence prediction network as a time sequence evolution mode feature of the network access targets; and performing network attack detection according to the time sequence evolution mode feature. The application can adapt to rapid dynamic changes of network attack modes by determining the time sequence evolution mode feature of the network access targets through the linear predictive coding spectrum features of the network access feature set, so that the precision of network attack detection and identification is improved.

The invention provides a control method of a liquid cooling system and electronic equipment, and can be applied to the technical field of heat dissipation. The control method of the liquid cooling system comprises the following steps: determining load grade information of electronic equipment in a target time period according to performance index data of the electronic equipment in the target time period; preprocessing the target data to obtain a plurality of sub-target data frames; performing linear predictive coding on the plurality of sub-target data frames to obtain a plurality of sub-linear predictive coefficients; based on the load level information, matching reference data from a preset health reference library; comparing the plurality of sub-target data frames and the plurality of sub-linear prediction coefficients with the matched reference data, and determining residual energy and coefficient distance corresponding to the target data; and determining a health index based on the residual energy and the coefficient distance corresponding to the target data, and generating early warning information based on the health index and a preset health threshold, the early warning information being used for prompting to perform a control operation corresponding to the early warning information on the liquid cooling system.

The application discloses a kind of based on machine learning electromechanical equipment noise reduction method, it is related to electromechanical equipment noise reduction field, including: the sound pressure and vibration signal when electromechanical equipment is operated are collected, and the operating state data of electromechanical equipment is collected;Noise characteristics are sparsified reconstruction;And the sound source positioning is carried out to noise characteristics after reconstruction using beam forming algorithm, obtain noise distribution chart;Noise reduction model based on deep learning is constructed, obtain the noise signal after noise reduction;The frequency in the preset frequency range of noise signal is extracted by peak search algorithm;The operating state data collected is analyzed using time-varying linear prediction coding algorithm TVLPC, obtain the noise frequency offset caused by electromechanical equipment operating state exception;Optimal solution is generated using particle swarm algorithm;According to the optimal solution of noise control, extract noise main frequency component to separate, obtain multiple phase inversion noise control signal.The precision of noise reduction control is improved in the application, compared with the prior art.

Methods, an encoder and a decoder are configured for transition between frames with different internal sampling rates. Linear predictive (LP) filter parameters are converted from a sampling rate S1 to a sampling rate S2. A power spectrum of a LP synthesis filter is computed, at the sampling rate S1, using the LP filter parameters. The power spectrum of the LP synthesis filter is modified to convert it from the sampling rate S1 to the sampling rate S2. The modified power spectrum of the LP synthesis filter is inverse transformed to determine autocorrelations of the LP synthesis filter at the sampling rate S2. The autocorrelations are used to compute the LP filter parameters at the sampling rate S2.

This application provides a control method and electronic device for a liquid cooling system, which can be applied to the field of heat dissipation technology. The control method includes: determining the load level information of the electronic device within a target time period based on performance index data; preprocessing the target data to obtain multiple sub-target data frames; performing linear prediction encoding on each of the multiple sub-target data frames to obtain multiple sub-linear prediction coefficients; matching benchmark data from a preset health benchmark library based on the load level information; comparing the multiple sub-target data frames and multiple sub-linear prediction coefficients with the matched benchmark data to determine the residual energy and coefficient distance corresponding to the target data; determining health indicators based on the residual energy and coefficient distance corresponding to the target data; generating early warning information based on the health indicators and a preset health threshold; and using the early warning information to prompt control operations corresponding to the early warning information on the liquid cooling system.