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In the context of music information retrieval, similarity-based approachesare useful for a variety of tasks that benefit from a query-by-examplescenario. Music however, naturally decomposes into a set of semanticallymeaningful factors of variation. Current representation learning strategiespursue the disentanglement of such factors from deep representations, resultingin highly interpretable models. This allows the modeling of music similarityperception, which is highly subjective and multi-dimensional. While the focusof prior work is on metadata driven notions of similarity, we suggest todirectly model the human notion of multi-dimensional music similarity. Toachieve this, we propose a multi-input deep neural network architecture, whichsimultaneously processes mel-spectrogram, CENS-chromagram and tempogram inorder to extract informative features for the different disentangled musicaldimensions: genre, mood, instrument, era, tempo, and key. We evaluated theproposed music similarity approach using a triplet prediction task and foundthat the proposed multi-input architecture outperforms a state of the artmethod. Furthermore, we present a novel multi-dimensional analysis in order toevaluate the influence of each disentangled dimension on the perception ofmusic similarity.

Multi-input Architecture and Disentangled Representation Learning for Multi-dimensional Modeling of Music Similarity