Few-shot musical source separation

Abstract

Deep learning-based approaches to musical source separation are often limited to the instrument classes that the models are trained on and do not generalize to separate unseen instruments. To address this, we propose a few-shot musical source separation paradigm. We condition a generic U-Net source separation model using few audio examples of the target instrument. We train a few-shot conditioning encoder jointly with the U-Net to encode the audio examples into a conditioning vector to configure the U-Net via feature-wise linear modulation (FiLM). We evaluate the trained models on real musical recordings in the MUSDB18 and MedleyDB datasets. We show that our proposed few-shot conditioning paradigm outperforms the baseline one-hot instrument-class conditioned model for both seen and unseen instruments. We further experiment with different conditioning example characteristics, including examples from different recordings, multi-sourced examples, and negative conditioning examples, to show the potential of applying the proposed few-shot approach to a wider variety of real-world scenarios.

Related

June 2023 | ICASSP

Contrastive Learning-based Audio to Lyrics Alignment for Multiple Languages

Simon Durand, Daniel Stoller, Sebastian Ewert

March 2023 | CLeaR - Causal Learning and Reasoning

Non-parametric identifiability and sensitivity analysis of synthetic control models

Jakob Zeitler, Athanasios Vlontzos, Ciarán Mark Gilligan-Lee

March 2023 | CLeaR - Causal Learning and Reasoning

Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding

Graham Van Goffrier, Lucas Maystre, Ciarán Mark Gilligan-Lee