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User-guided one-shot deep model adaptation for music source separation

Abstract : Music source separation is the task of isolating individual instruments which are mixed in a musical piece. This task is particularly challenging, and even state-of-the-art models can hardly generalize to unseen test data. Nevertheless, prior knowledge about individual sources can be used to better adapt a generic source separation model to the observed signal. In this work, we propose to exploit a temporal segmentation provided by the user, that indicates when each instrument is active, in order to fine-tune a pre-trained deep model for source separation and adapt it to one specific mixture. This paradigm can be referred to as user-guided one-shot deep model adaptation for music source separation, as the adaptation acts on the target song instance only. Our results are promising and show that state-of-the-art source separation models have large margins of improvement especially for those instruments which are underrepresented in the training data.
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Contributor : Giorgia Cantisani Connect in order to contact the contributor
Submitted on : Thursday, July 29, 2021 - 12:49:31 PM
Last modification on : Thursday, November 4, 2021 - 2:41:16 PM


  • HAL Id : hal-03219350, version 3



Giorgia Cantisani, Alexey Ozerov, Slim Essid, Gael Richard. User-guided one-shot deep model adaptation for music source separation. 2021. ⟨hal-03219350v3⟩



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