Source Separation for Hearing Aid Applications
The main focuses in this thesis are on blind separation of acoustic signals and on a speech enhancement by time-frequency masking. As a part of the thesis, an exhaustive review on existing techniques for blind separation of convolutive acoustic mixtures is provided. A new algorithm is proposed for separation of acoustic signals, where the number of sources in the mixtures exceeds the number of sensors. In order to segregate the sources from the mixtures, this method iteratively combines two techniques: Blind source separation by independent component analysis (ICA) and timefrequency masking. The proposed algorithm has been applied for separation of speech signals as well as stereo music signals. The proposed method uses recordings from two closely-spaced microphones, similar to the microphones used in hearing aids. Besides that, a source separation method known as gradient flow beamforming has been extended in order to cope with convolutive audio mixtures. This method also requires recordings from closely-spaced microphones. Also a theoretical result concerning the convergence in gradient descent independent component analysis algorithms is provided in the thesis.