  To achieve a flexible recommendation and retrieval system, it is desirable to
calculate music similarity by focusing on multiple partial elements of musical
pieces and allowing the users to select the element they want to focus on. A
previous study proposed using multiple individual networks for calculating
music similarity based on each instrumental sound, but it is impractical to use
each signal as a query in search systems. Using separated instrumental sounds
alternatively resulted in less accuracy due to artifacts. In this paper, we
propose a method to compute similarities focusing on each instrumental sound
with a single network that takes mixed sounds as input instead of individual
instrumental sounds. Specifically, we design a single similarity embedding
space with disentangled dimensions for each instrument, extracted by
Conditional Similarity Networks, which is trained by the triplet loss using
masks. Experimental results have shown that (1) the proposed method can obtain
more accurate feature representation than using individual networks using
separated sounds as input, (2) each sub-embedding space can hold the
characteristics of the corresponding instrument, and (3) the selection of
similar musical pieces focusing on each instrumental sound by the proposed
method can obtain human consent, especially in drums and guitar.
