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Article Dans Une Revue IEEE/ACM Transactions on Audio, Speech and Language Processing Année : 2016

On-the-fly audio source separation - a novel user-friendly framework

Résumé

This article addresses the challenging problem of single-channel audio source separation. We introduce a novel user-guided framework where source models that govern the separation process are learned on-the-fly from audio examples retrieved online. The user only provides the search keywords that describe the sources in the mixture. In this framework, the generic spectral characteristics of each source are modeled by a universal sound class model learned from the retrieved examples via non-negative matrix factorization. We propose several group sparsity-inducing constraints in order to efficiently exploit a relevant subset of the universal model adapted to the mixture to be separated. We then derive the corresponding multiplicative update rules for parameter estimation. Separation results obtained from automated and user tests on mixtures containing various types of sounds confirm the effectiveness of the proposed framework.
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Dates et versions

hal-01400990 , version 1 (24-11-2016)

Identifiants

  • HAL Id : hal-01400990 , version 1

Citer

Dalia El Badawy, Ngoc Q K Duong, Alexey Ozerov. On-the-fly audio source separation - a novel user-friendly framework. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2016. ⟨hal-01400990⟩
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