Abstract
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 information-theoretic 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.
Highlights
Similarity is fundamental to the perception and understanding 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
The compression-based model is deterministic and lacks any principled way of accounting for variability in similarity perception between or within participants
Summary
Similarity is fundamental to the perception and understanding of musical works. It is necessary for identifying repeated patterns within music, which in turn informs the perception of motifs, grouping structure and form. Similarity plays a fundamental role in Music Information Retrieval (MIR) where content-based retrieval of music requires a similarity measure to compute the distance between the query and potential matches in the datastore. Such methods have largely relied on the extraction of acoustic feature vectors from audio (e.g. MFCCs, chromagrams) and using machine learning methods to classify audio files into groups. Reviewing this research, Casey et al (2008) suggest that: ‘To improve the performance of MIR systems, the findings and methods of music perception and cognition could lead to better understanding of how humans interpret music and what humans expect from music searches’ (p. 692)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
