Single channel voice blind separation method based on computational auditory scene analysis

Abstract

The invention relates to a single channel voice blind separation method based on computational auditory scene analysis. The method includes the following steps of firstly, conducting front-end processing on inputted aliasing voice; secondly, conducting auditory feature extraction on the aliasing voice processed in the first step; thirdly, conducting auditory division on the aliasing voice processed in the second step on the basis of the harmonic features; fourthly, conducting auditory reconstruction on the aliasing voice processed in the third step on the basis of the energy features; fifthly, conducting target voice reconstruction on the aliasing voice processed in the fourth step. Under the condition that noise exists, the problem of single channel aliasing voice separation can be well solved, the separated voice can be applied to the front end of the voice recognition, and the single channel voice blind separation method can have wide application prospect in the artificial intelligence field, the voice communication field and the voice signal enhancement field.

Description

technical field [0001] The invention relates to a single-channel speech blind separation method based on computational auditory scene analysis, which belongs to the field of speech signal processing. Background technique [0002] Speech signal processing is one of the most eye-catching research fields in signal processing in recent years. In the field of speech signal processing, an important problem is how to separate the original speech from the aliased speech obtained by random mixing of unknown signals. The aliased speech Separation has important research significance and practical value in noise elimination, speech synthesis, speech communication and so on. Due to the overlap of aliased speech in the time domain and frequency domain, it is difficult for the commonly used speech enhancement methods to achieve speech separation. At present, the most commonly used method for aliasing speech separation is the blind source separation method, that is, the independent compone...

AI Technical Summary

Problems solved by technology

Due to the overlap of aliased speech in time domain and frequency domain, common speech enhancement methods are difficult to achieve speech separation

At present, the most commonly used method for aliasing speech separation is the blind source separation method, that is, the independent component analysis method, but the independent component analysis method has some assumptions, such as requiring that the number of observed signals is greater than or equal to the number of source signals, assuming no Noise, etc., these conditions limit the application of blind source separation, therefore, blind source separation cannot be applied to the single-channel aliasing speech separation problem where the number of source signals is greater than the number of observation signals, especially in the presence of noise, blind source separation The effect of the separation method applied to the separation of aliased speech is obviously deteriorated

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