@inproceedings{ed5092cc7527479abefefa0eb141ad3e,
title = "A Probabilistic Approach to Single Channel Blind Signal Separation",
abstract = "We present a new technique for achieving source separation when given only a single channel recording. The main idea is based on exploiting the inherent time structure of sound sources by learning a priori sets of basis filters in time domain that encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single channel data and sets of basis filters. For each time point we infer the source signals and their contribution factors. This inference is possible due to the prior knowledge of the basis filters and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation and our experimental results exhibit a high level of separation performance for mixtures of two music signals as well as the separation of two voice signals.",
author = "Jang, {Gil Jin} and Lee, {Te Won}",
note = "Publisher Copyright: {\textcopyright} NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems. All rights reserved.; 15th International Conference on Neural Information Processing Systems, NIPS 2002 ; Conference date: 09-12-2002 Through 14-12-2002",
year = "2002",
language = "English",
series = "NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems",
publisher = "MIT Press Journals",
pages = "1173--1180",
editor = "Suzanna Becker and Sebastian Thrun and Klaus Obermayer",
booktitle = "NIPS 2002",
address = "United States",
}