Abstract
A new method is proposed to realize the blind separation of speech signals under underdetermined conditions. Before estimating the mixed parameters, the single-source interval pre-extraction operation first "filters" out a part which is obviously not a single source. The time-frequency interval of the source analysis domain category.The simulation results show the good performance of the algorithm.
Highlights
How to identify the sound source of interest from a noisy environment and obtain a clear voice is an urgent problem to be solved
In the speech processing system, blind source separation is mostly used for speech recognition, that is, to separate and reconstruct the original speech signal from a large number of mixed speech signals [1]
Through research of TIFROM algorithm and TIFCORR algorithm based on the two single-source interval detection algorithms[4], it can be found that when it is used to deal with the under-determined blind source separation model, as the number of source signals increases, the two algorithms are Errors will occur in the search of intervals, and it is not guaranteed that the search results of a single source interval are optimal each time, resulting in poor algorithm performance [5,6]
Summary
How to identify the sound source of interest from a noisy environment and obtain a clear voice is an urgent problem to be solved. The strength of the signal and the change of intonation determine its non-stationarity, and the speech signal involved in this article is a typical non-stationary signal. Occasions, and people will change this non-stationary law and make it more difficult to solve the problem
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