  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. To extend the scope of our approach to a
wider variety of real-world scenarios, we also experiment with different
conditioning example characteristics, including examples from different
recordings, with multiple sources, or negative conditioning examples.
