Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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.
Complete list of metadata
Contributor : Giorgia Cantisani Connect in order to contact the contributor
Submitted on : Thursday, May 6, 2021 - 12:51:22 PM
Last modification on : Tuesday, October 19, 2021 - 11:16:31 AM
Long-term archiving on: : Saturday, August 7, 2021 - 6:50:36 PM


Files produced by the author(s)


  • HAL Id : hal-03219350, version 1


Giorgia Cantisani, Alexey Ozerov, Slim Essid, Gael Richard. User-guided one-shot deep model adaptation for music source separation. 2021. ⟨hal-03219350v1⟩



Les métriques sont temporairement indisponibles