  This paper addresses the problem of domain adaptation for the task of music
source separation. Using datasets from two different domains, we compare the
performance of a deep learning-based harmonic-percussive source separation
model under different training scenarios, including supervised joint training
using data from both domains and pre-training in one domain with fine-tuning in
another. We propose an adversarial unsupervised domain adaptation approach
suitable for the case where no labelled data (ground-truth source signals) from
a target domain is available. By leveraging unlabelled data (only mixtures)
from this domain, experiments show that our framework can improve separation
performance on the new domain without losing any considerable performance on
the original domain. The paper also introduces the Tap & Fiddle dataset, a
dataset containing recordings of Scandinavian fiddle tunes along with isolated
tracks for 'foot-tapping' and 'violin'.
