Source Separation for Hearing Aid Applications

Michael Syskind Pedersen

AbstractThe 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.
TypePh.D. thesis [Academic thesis]
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
NoteSupervised by professor Jan Larsen, IMM. Project was funded by the Oticon foundation.
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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