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Coding-based Informed Source Separation: Nonnegative Tensor Factorization Approach

Abstract : Informed source separation (ISS) aims at reliably recovering sources from a mixture. To this purpose, it relies on the assumption that the original sources are available during an encoding stage. Given both sources and mixture, a side-information may be computed and transmitted along with the mixture, whereas the original sources are forgotten. During a decoding stage, both mixture and side-information are processed to recover the sources. Most ISS techniques proposed so far rely on a source separation strategy and cannot achieve better results than oracle estimators. In this study, we introduce Coding-based ISS (CISS) and draw the connection between ISS and source coding. CISS amounts to encode the sources using not only a model as in source coding but also the observation of the mixture. This strategy has several advantages. First, it can reach any quality, provided sufficient bandwidth is available as in source coding. Second, it makes use of the mixture in order to reduce the bitrate required to transmit the sources, as in classical ISS. Furthermore, we introduce Nonnegative Tensor Factorization as a very efficient model for CISS and report rate-distortion results that strongly outperform the state of the art.
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Contributor : Alexey Ozerov Connect in order to contact the contributor
Submitted on : Friday, October 4, 2013 - 5:10:58 PM
Last modification on : Monday, October 12, 2020 - 7:53:54 PM
Long-term archiving on: : Friday, April 7, 2017 - 6:45:57 AM


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  • HAL Id : hal-00734022, version 3



Alexey Ozerov, Antoine Liutkus, Roland Badeau, Gael Richard. Coding-based Informed Source Separation: Nonnegative Tensor Factorization Approach. [Research Report] 2012, pp.18. ⟨hal-00734022v3⟩



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