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Speech signal sparse representation method based on convolution framework

A speech signal, sparse representation technology, applied in speech analysis, instruments, etc., can solve the problem of low data adaptability

Active Publication Date: 2018-09-28
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0004] Note that the local basis and non-local basis in the convolution framework are generally selected as basis matrices with structural characteristics, such as Fourier basis, cosine basis and wavelet basis. Although their structure is simple, the data adaptability is low

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  • Speech signal sparse representation method based on convolution framework
  • Speech signal sparse representation method based on convolution framework
  • Speech signal sparse representation method based on convolution framework

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[0082] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described examples are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0083] The present invention provides a sparse representation method for speech signals based on a convolution framework. First, an optimization model of the convolution framework is established for a given non-local basis and a speech training signal set; figure 2 Shown) to realize the optimization training of the convolution framework, refer to figure 1 , figure 1 Flowchart for optimizing the training convolutional framework using the speech training signal set....

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Abstract

The invention discloses a speech signal sparse representation method based on a convolution framework. According to a given non-local base U in the convolution framework, an optimal model of a local base V is established by using a speech training signal set, after solving the model numerically, a local base which is best matched with the non-local base is obtained, and the convolution framework of optimized training is obtained. The convolution framework after optimization training can fully explore the local and non-local information of a speech signal to be expressed, and has better speechsignal sparse representation ability. The convolution framework after optimization training can make use of the structural features of the speech training signal set and has better data adaptability.

Description

technical field [0001] The invention relates to a speech signal sparse representation method, in particular to a speech signal sparse representation method based on a convolution framework. Background technique [0002] Sparse representation of speech signals has important applications in the fields of speech signal denoising, speech restoration, etc., which stems from a basic fact: natural signals have sparse characteristics, that is, when the signal is linearly decomposed on a representation base (dictionary), its representation coefficient is sparse. The Fourier base, cosine base, wavelet base, etc. in harmonic analysis provide important mathematical tools for the sparse representation of speech signals. Although its structure is simple and the amount of calculation is small, the sparse representation ability is limited, so the overcomplete base (dictionary) Came into being. The over-complete dictionary enhances the sparse representation ability, but the training is com...

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Application Information

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IPC IPC(8): G10L25/27
CPCG10L25/27
Inventor 王泽龙袁翰刘吉英叶钒余奇严奉霞朱炬波
Owner NAT UNIV OF DEFENSE TECH