Blind separation of speech signals based on wavelet transform and independent component analysis

Abstract

Abstract：Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.

A Method about the Worn Materials Applied to the Marine Mechanical and Electronic Equipments in Condition Monitoring

“…Some of the mixed signals are composed of ones linear together, which are independent themselves. They would be separated from the mixed signals by the method of ICA [4] . In the friction pairs, together with the worn elements from the other circumstances, all of them are thought to be independent.…”

Blind Separation of Excavator Noise Signals in Frequency Domain

“…It is very difficult to separate convolutive signals effectively by traditional Fourier transform and wavelet transform. But there is a new way to do so, it is independent component analysis (ICA), a widely applied blind source separation approach during recently years [6,7].…”