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Weakly Informed Audio Source Separation

Abstract : Prior information about the target source can improve audio source separation quality but is usually not available with the necessary level of audio alignment. This has limited its usability in the past. We propose a separation model that can nevertheless exploit such weak information for the separation task while aligning it on the mixture as a byproduct using an attention mechanism. We demonstrate the capabilities of the model on a singing voice separation task exploiting artificial side information with different levels of expres-siveness. Moreover, we highlight an issue with the common separation quality assessment procedure regarding parts where targets or predictions are silent and refine a previous contribution for a more complete evaluation.
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Preprints, Working Papers, ...
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Contributor : Kilian Schulze-Forster Connect in order to contact the contributor
Submitted on : Friday, October 25, 2019 - 7:11:05 AM
Last modification on : Tuesday, March 8, 2022 - 5:46:02 PM
Long-term archiving on: : Sunday, January 26, 2020 - 1:10:23 PM


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  • HAL Id : hal-02332689, version 1



Kilian Schulze-Forster, Clément Doire, Gael Richard, Roland Badeau. Weakly Informed Audio Source Separation. 2019. ⟨hal-02332689⟩



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