Human and automated judgements of musical similarity in a global sample

Human and automated judgements of musical similarity in a global sample

Presenter Name:Hideo Daikoku

School/Affiliation:Keio University, Japan

Co-Authors:Hideo Daikoku, Shenghao Ding, Ujwal Sriharsha Sanne, Emmanouil Benetos, Anna Lomax Chairetakis Wood, Shinya Fujii, Patrick E. Savage

Abstract:While music information retrieval (MIR) has made substantial progress in automatic analysis of audio similarity for Western music, it remains unclear whether these algorithms can be meaningfully applied to cross-cultural analyses of more diverse samples. Here we collected perceptual ratings from 62 participants using a global sample of 30 traditional songs, and compared these ratings against both pre-existing expert annotations and state-of-the-art audio similarity algorithms. We found that different methods of perceptual ratings all produced similar, moderate levels of inter-rater reliability comparable to previous studies, but that agreement between human and automated methods was always low regardless of the specific methods used to calculate musical similarity. Our findings suggest that current MIR methods are unable to measure cross-cultural music similarity in perceptually meaningful ways. We propose future directions to enable meaningful automatic analysis of all the world’s music.

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