Voice enhancement method based on noise classification optimization IMCRA algorithm

A technology for speech enhancement and noise classification, applied in speech analysis, neural learning methods, biological neural network models, etc. Enhanced effect, improved intelligibility effect
CN112133322APending Publication Date: 2020-12-25NANTONG SAIYANG ELECTRONICS CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
NANTONG SAIYANG ELECTRONICS CO LTD
Publication Date
2020-12-25

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Abstract

The invention discloses a voice enhancement method based on a noise classification optimization IMCRA algorithm. The method comprises the following steps of: searching an optimal alpha s, alpha d andalpha parameter combination for different noise types, alpha s and alpha d being two smooth parameters of the IMCRA algorithm during the estimation of a noise power spectrum, and alpha being a weightparameter of priori signal-to-noise ratio estimation; classifying noise types of input signals by using a convolutional neural network CNN; according to different noise types, selecting different parameter groups alpha s, alpha d and alpha according to the optimization result in the step 1; and according to the parameter group selected in the step 3, carrying out voice enhancement on a test set signal by using an IMCRA method to obtain a finally enhanced voice. The method has a better voice enhancement effect, and the quality and intelligibility of the enhanced voice are improved.
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Description

technical field

[0001] The invention relates to a speech enhancement method, in particular to a speech enhancement method based on noise classification optimization IMCRA algorithm, which belongs to the field of speech processing. Background technique

[0002] In the process of speech signal processing, the problem of noise pollution is unavoidable. Therefore, how to effectively suppress noise and improve the quality and intelligibility of speech signals has become a hot topic of research by many scholars. At present, a variety of speech enhancement algorithms have been proposed, mainly including methods based on signal processing, methods based on model training and methods based on statistical models.

[0003] Among the methods based on signal processing, spectral subtraction and Wiener filtering are two most representative techniques. In the case of correctly estimating the background noise, this type of method can achieve better separation performance. However, under ...

Claims

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