  In the context of music information retrieval, similarity-based approaches
are useful for a variety of tasks that benefit from a query-by-example
scenario. Music however, naturally decomposes into a set of semantically
meaningful factors of variation. Current representation learning strategies
pursue the disentanglement of such factors from deep representations, resulting
in highly interpretable models. This allows the modeling of music similarity
perception, which is highly subjective and multi-dimensional. While the focus
of prior work is on metadata driven notions of similarity, we suggest to
directly model the human notion of multi-dimensional music similarity. To
achieve this, we propose a multi-input deep neural network architecture, which
simultaneously processes mel-spectrogram, CENS-chromagram and tempogram in
order to extract informative features for the different disentangled musical
dimensions: genre, mood, instrument, era, tempo, and key. We evaluated the
proposed music similarity approach using a triplet prediction task and found
that the proposed multi-input architecture outperforms a state of the art
method. Furthermore, we present a novel multi-dimensional analysis in order to
evaluate the influence of each disentangled dimension on the perception of
music similarity.
