Disentangled Multidimensional Metric-Learning for Music Similarity

Music similarity search is useful for a variety of creative tasks such as replacing one music recording with another recording with a similar “feel”, a common task in video editing. For this task, it is typically necessary to define a similarity met- ric to compare one recording to another. Music similarity, however, is hard to define and depends on multiple simul- taneous notions of similarity (i.e. genre, mood, instrument, tempo). While prior work ignore this issue, we embrace this idea and introduce the concept of multidimensional similarity and unify both global and specialized similarity metrics into a single, semantically disentangled multidimensional similarity metric.

Dim-Sim Dataset

The Dim-Sim dataset page is here: https:jongpillee.github.io/multi-dim-music-sim/.