NDLI: Localisation-based, situation-adaptive mask generation for source separation

Content Provider	IEEE Xplore Digital Library
Author	Madhu, N. Wouters, J.
Copyright Year	2010
Description	Author affiliation: ExpORL, Dept. Neurosciences, Katholieke Universiteit, Leuven, B-3000, Belgium (Madhu, N.; Wouters, J.)
Abstract	Presented is a microphone-array based approach for the extraction of individual signals from a mixture of competing sources and background noise. Source separation is done using data-driven soft-masks, the parameters for the estimation of these masks being obtained from an extension of a recently proposed source localisation and tracking framework. The separation algorithm is applicable to any arbitrary array - allowing for its integration into a wide variety of applications. The advantage of the proposed mask generation over state-of-the-art mask-based algorithms is the implicit scalability with respect to the number of microphones (M), the number of sources (Q), spatial source spread, and reverberation - obviating the need for heuristic adaptation of the mask generation to different acoustical scenarios. The individual signals extracted using the soft-masks evince low amounts of musical noise. Smoothing these masks in their cepstral representation further reduces the musical noise phenomenon whilst preserving the signal of interest, thereby improving the listening experience.
Starting Page	1
Ending Page	6
File Size	11893025
Page Count	6
File Format	PDF
ISBN	9781424462858
e-ISBN	9781424462872
DOI	10.1109/ISCCSP.2010.5463383
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-03
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Source separation Text analysis Recurrent neural networks Speech analysis Synthesizers Natural languages Neural networks Signal processing algorithms Speech synthesis Data mining
Content Type	Text
Resource Type	Article

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