  The criteria for measuring music similarity are important for developing a
flexible music recommendation system. Some data-driven methods have been
proposed to calculate music similarity from only music signals, such as metric
learning based on a triplet loss using tag information on each musical piece.
However, the resulting music similarity metric usually captures the entire
piece of music, i.e., the mixing of various instrumental sound sources,
limiting the capability of the music recommendation system, e.g., it is
difficult to search for a musical piece containing similar drum sounds. Towards
the development of a more flexible music recommendation system, we propose a
music similarity calculation method that focuses on individual instrumental
sound sources in a musical piece. By fully exploiting the potential of
data-driven methods for our proposed method, we employ weakly supervised metric
learning to individual instrumental sound source signals without using any tag
information, where positive and negative samples in a triplet loss are defined
by whether or not they are from the same musical piece. Furthermore, assuming
that each instrumental sound source is not always available in practice, we
also investigate the effects of using instrumental sound source separation to
obtain each source in the proposed method. Experimental results have shown that
(1) unique similarity metrics can be learned for individual instrumental sound
sources, (2) similarity metrics learned using some instrumental sound sources
are possible to lead to more accurate results than that learned using the
entire musical piece, (3) the performance degraded when learning with the
separated instrumental sounds, and (4) similarity metrics learned by the
proposed method well produced results that correspond to perception by human
senses.
