Voice enhancing method based on multiresolution auditory cepstrum coefficient and deep convolutional neural network

Abstract

The invention discloses a voice enhancing method based on a multiresolution auditory cepstrum system and a deep convolutional neural network. The voice enhancing method comprises the following steps:firstly, establishing new characteristic parameters, namely multiresolution auditory cepstrum coefficient (MR-GFCC), capable of distinguishing voice from noise; secondly, establishing a self-adaptivemasking threshold on based on ideal soft masking (IRM) and ideal binary masking (IBM) according to noise variations; further training an established seven-layer neural network by using new extracted characteristic parameters and first / second derivatives thereof and the self-adaptive masking threshold as input and output of the deep convolutional neural network (DCNN); and finally enhancing noise-containing voice by using the self-adaptive masking threshold estimated by the DCNN. By adopting the method, the working mechanism of human ears is sufficiently utilized, voice characteristic parameters simulating a human ear auditory physiological model are disposed, and not only is a relatively great deal of voice information maintained, but also the extraction process is simple and feasible.

Description

technical field [0001] The invention belongs to the technical field of speech signal processing, and relates to a speech enhancement method based on multi-resolution auditory cepstral coefficients and a deep convolutional neural network. Background technique [0002] Speech enhancement technology refers to extracting the purest possible speech signal from the noise background, enhancing the useful speech signal, and suppressing and reducing noise interference when the speech signal is interfered or even submerged by various noises (including speech). technology. Due to the randomness of interference, it is almost impossible to extract completely pure speech signal from noisy speech. In this case, there are two main purposes of speech enhancement: one is to improve speech quality, eliminate background noise, so that the listener is willing to accept it without feeling tired, which is a subjective measure; the other is to improve the intelligibility of speech , which is an o...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to propose a method based on multi-resolution cepstral coefficients and deep convolutional neural network for the unsatisfactory performance of the current speech enhancement algorithm under non-stationary noise and the problems existing in the extraction process of speech feature parameters. Combined Speech Enhancement Technology

Then, the adaptive masking threshold based on ideal soft masking (IRM) and ideal binary masking (IBM) was constructed by tracking noise changes; then, the deep convolutional neural network (DCNN) model in deep learning has the ability to extract complex features, Good at modeling structured information in data and estimating adaptive masking thresholds, which can solve the problem of unsatisfactory performance of traditional speech enhancement algorithms in non-stationary noise environments

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