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a based on l 1/2 Speech denoising method and system for sparse constrained convolutional non-negative matrix factorization

A non-negative matrix decomposition and voice denoising technology, which is applied in voice analysis, instruments, etc., can solve problems such as noise pollution and inaudible content

Active Publication Date: 2019-10-08
ANHUI UNIVERSITY
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Problems solved by technology

[0002] Speech is an important carrier of daily communication, but it is often polluted by various noises in the real environment, making it difficult for people to hear the content clearly

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  • a based on l <sub>1/2</sub> Speech denoising method and system for sparse constrained convolutional non-negative matrix factorization
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  • a based on l <sub>1/2</sub> Speech denoising method and system for sparse constrained convolutional non-negative matrix factorization

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Embodiment Construction

[0064] The present invention will be further described below in combination with specific embodiments and accompanying drawings.

[0065] The present invention is based on L 1 / 2 Sparse Constrained Convolutional Nonnegative Matrix Factorization (hereinafter referred to as "CNMF_L 1 / 2 ”) speech denoising method, figure 1 It is the general flowchart of the denoising of the present invention. The overall input is a certain type of noise and the speech after mixed noise, where the noise can be of different types (such as stationary noise, non-stationary noise, etc.); the output is the speech after denoising.

[0066] figure 2 is the flow chart of the noise training process in step 1.

[0067] Step 1.1, perform short-time Fourier transform (Short-Time Fourier Transform, STFT) transformation on the noise to obtain its amplitude spectrum N.

[0068] Step 1.2, perform CNMF decomposition on the noise amplitude spectrum to obtain the noise basis W n And its corresponding coefficie...

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Abstract

The invention discloses a 1 / 2 Speech denoising method and system for sparsely constrained convolutional nonnegative matrix factorization. In single-channel speech enhancement, it is assumed that the noisy speech signal v(i) is additively uncorrelated between the noise signal n(i) and the speech signal s(i), that is, v(i)=n(i)+s(i) , use the CNMF method to train the specific noise to obtain the noise base information; then use the noise base as the prior information, use CNMF_L 1 / 2 The method decomposes the noisy language to obtain the speech basis, and finally synthesizes the denoised speech. The method of the present invention can describe the correlation of speech between frames better; And use L 1 / 2 The regularization term imposes strong sparsity constraints on the speech base coefficient matrix, which can realize that the separated speech contains less residual noise. Compared with traditional methods such as spectral subtraction, Wiener filtering method and minimum mean square error logarithmic domain spectral estimation method, it can improve the intelligibility of the enhanced speech.

Description

technical field [0001] The invention belongs to the field of acoustic signal processing, in particular to a 1 / 2 Speech denoising method and system for sparsely constrained convolutional nonnegative matrix factorization. Background technique [0002] Speech is an important carrier of daily communication, but it is often polluted by various noises in the real environment, making it difficult for people to hear the content clearly. Speech enhancement extracts as clean speech as possible from the polluted speech signal by suppressing or eliminating these noises, so as to obtain intelligible speech. Speech enhancement technology is often used in the fields of speech recognition, speech coding and intelligent communication. [0003] The speech enhancement method based on Non-negative matrix factorization (NMF) is a part-based expression method. By decomposing the speech signal, the basis vector and coefficient matrix representing speech characteristics can be obtained. At prese...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G10L21/0272
Inventor 周健路成
Owner ANHUI UNIVERSITY
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