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.
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© 2012 Springer-Verlag Berlin Heidelberg
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Deleforge, A., Horaud, R. (2012). A Latently Constrained Mixture Model for Audio Source Separation and Localization. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_46
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DOI: https://doi.org/10.1007/978-3-642-28551-6_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28550-9
Online ISBN: 978-3-642-28551-6
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