A latently constrained mixture model for audio source separation and localization (2012)
| Venue: | In Latent Variable Analysis and Signal Separation, Tel Aviv |
| Citations: | 2 - 2 self |
BibTeX
@INPROCEEDINGS{Deleforge12alatently,
author = {Antoine Deleforge and Radu Horaud},
title = {A latently constrained mixture model for audio source separation and localization},
booktitle = {In Latent Variable Analysis and Signal Separation, Tel Aviv},
year = {2012}
}
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Abstract
Abstract. We present a method for audio source separation and localization from binaural recordings. The method combines a new generative probabilistic model with time-frequency masking. We suggest that device-dependent relationships between point-source positions and interaural spectral cues may be learnt in order to constrain a mixture model. This allows to capture subtle separation and localization features embedded in the auditory data. We illustrate our method with data composed of two and three mixed speech signals in the presence of reverberations. Using standard evaluation metrics, we compare our method with a recent binaural-based source separation-localization algorithm. 1
Citations
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| 9 | V.: Sound source localization in real sound fields based on empirical statistics of interaural parameters - Nix, Hohmann - 2006 |
| 8 | Model-based expectationmaximization source separation and localization - Mandel, Weiss, et al. - 2010 |
| 5 | A source localization/separation/respatialization system based on unsupervised classification of interaural cues - Mouba, Marchand |
