Compression-based Modelling of Musical Similarity Perception

Pearce, Marcus and Müllensiefen, Daniel. 2017. Compression-based Modelling of Musical Similarity Perception. Journal of New Music Research, 46(2), pp. 135-155. ISSN 0929-8215 [Article]

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Abstract or Description

Similarity is an important concept in music cognition research since the similarity between (parts of) musical pieces determines perception of stylistic categories and structural relationships between parts of musical works. The purpose of the present research is to develop and test models of musical similarity perception inspired by a transformational approach which conceives of similarity between two perceptual objects in terms of the complexity of the cognitive operations required to transform the representation of the first object into that of the second, a process which has been formulated in informationtheoretic terms. Specifically, computational simulations are developed based on compression distance in which a probabilistic model is trained on one piece of music and then used to predict, or compress, the notes in a second piece. The more predictable the second piece according to the model, the more efficiently it can be encoded and the greater the similarity between the two pieces. The present research extends an existing information-theoretic model of auditory expectation (IDyOM) to compute compression distances varying in symmetry and normalisation using high-level symbolic features representing aspects of pitch and rhythmic structure. Comparing these compression distances with listeners’ similarity ratings between pairs of melodies collected in three experiments demonstrates that the compression-based model provides a good fit to the data and allows the identification of representations, model parameters and compression-based metrics that best account for musical similarity perception.

The compression-based model also shows comparable performance to the best-performing algorithms on the MIREX
2005 melodic similarity task.

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Similarity, timing, representation, perception, machine learning, information retrieval

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3 March 2017Accepted
30 March 2017Published

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Date Deposited:

16 Jun 2017 14:44

Last Modified:

29 Apr 2020 16:27


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