  In the field of music information retrieval (MIR), cover song identification
(CSI) is a challenging task that aims to identify cover versions of a query
song from a massive collection. Existing works still suffer from high
intra-song variances and inter-song correlations, due to the entangled nature
of version-specific and version-invariant factors in their modeling. In this
work, we set the goal of disentangling version-specific and version-invariant
factors, which could make it easier for the model to learn invariant music
representations for unseen query songs. We analyze the CSI task in a
disentanglement view with the causal graph technique, and identify the
intra-version and inter-version effects biasing the invariant learning. To
block these effects, we propose the disentangled music representation learning
framework (DisCover) for CSI. DisCover consists of two critical components: (1)
Knowledge-guided Disentanglement Module (KDM) and (2) Gradient-based
Adversarial Disentanglement Module (GADM), which block intra-version and
inter-version biased effects, respectively. KDM minimizes the mutual
information between the learned representations and version-variant factors
that are identified with prior domain knowledge. GADM identifies
version-variant factors by simulating the representation transitions between
intra-song versions, and exploits adversarial distillation for effect blocking.
Extensive comparisons with best-performing methods and in-depth analysis
demonstrate the effectiveness of DisCover and the and necessity of
disentanglement for CSI.
