Single-channel speech enhancement method based on joint dictionary learning and sparse representation

A sparse representation, speech enhancement technology, applied in speech analysis, complex mathematical operations, instruments, etc., can solve the performance limitations of speech enhancement, rarely use phase information and other problems, to increase the time-frequency representation ability, reduce estimation error, quality boosted effect

Active Publication Date: 2020-08-07
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

However, most algorithms only use the amplitude information of the speech frequency spect...

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  • Single-channel speech enhancement method based on joint dictionary learning and sparse representation
  • Single-channel speech enhancement method based on joint dictionary learning and sparse representation
  • Single-channel speech enhancement method based on joint dictionary learning and sparse representation

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Embodiment Construction

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0013] Different from most of the existing single-channel speech enhancement algorithms, the embodiment of the present invention provides a single-channel speech enhancement method based on joint dictionary learning and sparse representation. This method first decomposes the single-channel signal into a group of sub-signals, and increases the signal Representation ability; secondly, make full use of the amplitude, real part and imaginary part inform...

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Abstract

The invention provides a single-channel speech enhancement method based on joint dictionary learning and sparse representation. Carrying out dual-tree complex wavelet transform on the clean voice to obtain a group of sub-band signals, carrying out short-time Fourier transform on the sub-band signals to obtain a time-frequency spectrum of the sub-band signals, learning a joint dictionary of the clean voice by utilizing the amplitude, the real part, the imaginary part and the voice sparsity of the sub-band signals, and learning a joint dictionary of the clean noise as well; carrying out dual-tree complex wavelet transform and short-time Fourier transform on the noisy speech; obtaining a time-frequency spectrum of each sub-band signal; phase and real part imaginary part symbols are reserved;amplitude, real part and imaginary part absolute values are extracted and projected on the clean voice and clean noise joint dictionary; according to the method, the sparse representation coefficientsof the voice and the noise are obtained, the final estimation of the sub-band voice time-frequency spectrum is obtained by using the coefficients, the time-frequency spectrum phase, the real part imaginary part symbol, the mask, the weight and the like, and the enhanced voice signal is obtained by performing short-time inverse Fourier transform and dual-tree complex wavelet inverse transform, sothat the voice enhancement capability is improved.

Description

technical field [0001] The invention relates to the field of single-channel speech enhancement, in particular to a single-channel speech enhancement method based on joint dictionary learning and sparse representation. Background technique [0002] Voice plays a leading role in people's communication activities. Due to the pollution of various noises, the human ear and related smart devices receive noisy speech, and the quality and intelligibility of speech will be significantly reduced, which affects people's subjective auditory experience and the speech recognition rate of smart devices. Speech enhancement technology is the main method to solve this kind of problems. How to restore clean speech from noisy speech has always been a hot issue in the field of signal processing. In recent years, researchers have successively proposed some single-channel speech enhancement algorithms based on dictionary learning and sparse representation. These algorithms take advantage of the ...

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Application Information

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IPC IPC(8): G10L21/0216G10L25/30G06F17/14
CPCG10L21/0216G10L25/30G06F17/14
Inventor 叶中付穆罕默德 绍希杜尔·伊斯兰姆
Owner UNIV OF SCI & TECH OF CHINA
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