@inproceedings{7edaa1cec0cd48b08374f7a56d0a802e,
title = "Low-artifact source separation using probabilistic latent component analysis",
abstract = "We propose a method based on the probabilistic latent component analysis (PLCA) in which we use exponential distributions as priors to decrease the activity level of a given basis vector. A straightforward application of this method is when we try to extract a desired source from a mixture with low artifacts. For this purpose, we propose a maximum a posteriori (MAP) approach to identify the common basis vectors between two sources. A low-artifact estimate can now be obtained by using a constraint such that the common basis vectors in the interfering signal's dictionary tend to remain inactive. We discuss applications of this method in source separation with similar-gender speakers and in enhancing a speech signal that is contaminated with babble noise. Our simulations show that the proposed method not only reduces the artifacts but also increases the overall quality of the estimated signal.",
keywords = "Artifact Reduction, Dictionary Learning, Nonnegative Matrix Factorization (NMF), PLCA, Source Separation",
author = "Nasser Mohammadiha and Paris Smaragdis and Arne Leijon",
year = "2013",
doi = "10.1109/WASPAA.2013.6701837",
language = "English (US)",
isbn = "9781479909728",
series = "IEEE Workshop on Applications of Signal Processing to Audio and Acoustics",
booktitle = "2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2013",
note = "2013 14th IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2013 ; Conference date: 20-10-2013 Through 23-10-2013",
}