PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Separating Underdetermined Convolutive Speech Mixtures
Michael Pedersen, DeLiang Wang, Jan Larsen and Ulrik Kjems
In: ICA 2006, March 2006, Charleston, SC, USA.


A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors. In many real-world applications these limitations are too restrictive. We propose a method for overcomplete blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation techniques with binary time-frequency masking. In the proposed method, the number of source signals is not assumed to be known in advance and the number of sources is not limited to the number of microphones. Our approach needs only two microphones and the separated sounds are maintained as stereo signals.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Theory & Algorithms
ID Code:1491
Deposited By:Michael Pedersen
Deposited On:22 November 2006