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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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https://hal.telecom-paris.fr/hal-03219350
Contributor : Giorgia Cantisani <>
Submitted on : Wednesday, June 2, 2021 - 11:37:27 AM
Last modification on : Saturday, June 5, 2021 - 3:11:49 AM

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  • HAL Id : hal-03219350, version 2

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Giorgia Cantisani, Alexey Ozerov, Slim Essid, Gaël Richard. User-guided one-shot deep model adaptation for music source separation. 2021. ⟨hal-03219350v2⟩

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