Bayesian Unification of Sound Source Localization and Separation with Permutation Resolution

Takuma Otsuka, Katsuhiko Ishiguro, Hiroshi Sawada, Hiroshi G. Okuno

AAAI 2012 pp. 2038-2045

doi:10.1609/AAAI.V26I1.8376 /aaai/2012/otsuka2012aaai-bayesian/

Abstract

Sound source localization and separation with permutation resolution are essential for achieving a computational auditory scene analysis system that can extract useful information from a mixture of various sounds. Because existing methods cope separately with these problems despite their mutual dependence, the overall result with these approaches can be degraded by any failure in one of these components. This paper presents a unified Bayesian framework to solve these problems simultaneously where localization and separation are regarded as a clustering problem. Experimental results confirm that our method outperforms state-of-the-art methods in terms of the separation quality with various setups including practical reverberant environments.

PDF AAAI Semantic Scholar

Cite

Text

Otsuka et al. "Bayesian Unification of Sound Source Localization and Separation with Permutation Resolution." AAAI Conference on Artificial Intelligence, 2012. doi:10.1609/AAAI.V26I1.8376

Markdown

[Otsuka et al. "Bayesian Unification of Sound Source Localization and Separation with Permutation Resolution." AAAI Conference on Artificial Intelligence, 2012.](https://mlanthology.org/aaai/2012/otsuka2012aaai-bayesian/) doi:10.1609/AAAI.V26I1.8376

BibTeX

@inproceedings{otsuka2012aaai-bayesian,
  title     = {{Bayesian Unification of Sound Source Localization and Separation with Permutation Resolution}},
  author    = {Otsuka, Takuma and Ishiguro, Katsuhiko and Sawada, Hiroshi and Okuno, Hiroshi G.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2012},
  pages     = {2038-2045},
  doi       = {10.1609/AAAI.V26I1.8376},
  url       = {https://mlanthology.org/aaai/2012/otsuka2012aaai-bayesian/}
}