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Phase-dependent anisotropic Gaussian model for audio source separation
- Paul Magron #1, Roland Badeau #1, Bertrand David #1
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Laboratoire traitement et communication de l'information (LTCI)
- Télécm ParisTech
- Institut Mines-Télécom
- Université Paris-Saclay
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- References
- 42nd International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, IEEE, March 2017,
- Abstract
Phase reconstruction of complex components in the time-frequency domain is a challenging but necessary task for audio source separation. While traditional approaches do not exploit phase constraints that originate from signal modeling, some prior information about the phase can be obtained from sinusoidal modeling. In this paper, we introduce a probabilistic mixture model which allows us to incorporate such phase priors within a source separation framework. While the magnitudes are estimated beforehand, the phases are modeled by Von Mises random variables whose location parameters are the phase priors. We then approximate this non-tractable model by an anisotropic Gaussian model, in which the phase dependencies are preserved. This enables us to derive an MMSE estimator of the sources which optimally combines Wiener filtering and prior phase estimates. Experimental results highlight the potential of incorporating phase priors into mixture models for separating overlapping components in complex audio mixtures.
- Keywords
- Phase reconstruction, Von Mises distribution, anisotropic Gaussian model, phase unwrapping, source separation
- Category
- Paper in proceedings
- Research Area(s)
- Engineering Sciences/Signal and Image processing
- Identifier(s)
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HAL ref. hal-01416355
Bibliographic key PM:ICASSP-17
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- Last update
- on march 20, 2017 by Roland Badeau
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