Blind Source Separation of audio signals using independent component analysis and wavelets

In this work we proposed a new method that allows the blind source separation by the analysis of independent components known as FASTICA in the domain of Wavelet to observe his behavior on signs captured in a real environment. The problem that tries to be solved in Blind Source Separation (BSS) consists of recovering signs statistically independent. Nevertheless, certain difficulties appear when this system is applied to real signs, on the one hand the effect of the reverberation does that the mixtures gathered by the microphones are convolution mix; and on the other hand, these mixtures will not be totally independent. We did two experiments. With the first experiment we separated 2 audio signals with a very low percentage of error. With the second experiment we recorded 3 different audio sources with an array of 3 microphones, and then from one audio recorded source 3 signals were separated, we appreciate that in each source one signal was amplified and the other two signals were fallen down. From the results, the method that we proposed is able to separate from one mixed audio signal 2 or even 3 independent signals.