Skip to Main content Skip to Navigation
Conference 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

https://hal.telecom-paris.fr/hal-03219350
Contributor : giorgia cantisani Connect in order to contact the contributor
Submitted on : Thursday, July 29, 2021 - 12:49:31 PM
Last modification on : Monday, January 3, 2022 - 3:04:54 AM

Identifiers

  • HAL Id : hal-03219350, version 3

Collections

Citation

Giorgia Cantisani, Alexey Ozerov, Slim Essid, Gael Richard. User-guided one-shot deep model adaptation for music source separation. 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE, Oct 2021, New Paltz, NY, United States. ⟨hal-03219350v3⟩

Share

Metrics

Record views

370

Files downloads

455