{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T12:49:56Z","timestamp":1760014196062},"reference-count":11,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Semantic Computing"],"published-print":{"date-parts":[[2016,3]]},"abstract":"<jats:p> This paper proposes a novel concept we call musical commonness, which is the similarity of a song to a set of songs; in other words, its typicality. This commonness can be used to retrieve representative songs from a set of songs (e.g. songs released in the 80s or 90s). Previous research on musical similarity has compared two songs but has not evaluated the similarity of a song to a set of songs. The methods presented here for estimating the similarity and commonness of polyphonic musical audio signals are based on a unified framework of probabilistic generative modeling of four musical elements (vocal timbre, musical timbre, rhythm, and chord progression). To estimate the commonness, we use a generative model trained from a song set instead of estimating musical similarities of all possible song-pairs by using a model trained from each song. In experimental evaluation, we used two song-sets: 3278 Japanese popular music songs and 415 English songs. Twenty estimated song-pair similarities for each element and each song-set were compared with ratings by a musician. The comparison with the results of the expert ratings suggests that the proposed methods can estimate musical similarity appropriately. Estimated musical commonnesses are evaluated on basis of the Pearson product-moment correlation coefficients between the estimated commonness of each song and the number of songs having high similarity with the song. Results of commonness evaluation show that a song having higher commonness is similar to songs of a song set. <\/jats:p>","DOI":"10.1142\/s1793351x1640002x","type":"journal-article","created":{"date-parts":[[2016,6,8]],"date-time":"2016-06-08T00:04:27Z","timestamp":1465344267000},"page":"27-52","source":"Crossref","is-referenced-by-count":4,"title":["Musical Similarity and Commonness Estimation Based on Probabilistic Generative Models of Musical Elements"],"prefix":"10.1142","volume":"10","author":[{"given":"Tomoyasu","family":"Nakano","sequence":"first","affiliation":[{"name":"National Institute of Advanced Industrial, Science and Technology (AIST), Ibaraki 305-8568, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuyoshi","family":"Yoshii","sequence":"additional","affiliation":[{"name":"Kyoto University, Kyoto 606-8501, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masataka","family":"Goto","sequence":"additional","affiliation":[{"name":"National Institute of Advanced Industrial, Science and Technology (AIST), Ibaraki 305-8568, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2016,6,7]]},"reference":[{"key":"S1793351X1640002XBIB001","doi-asserted-by":"publisher","DOI":"10.1250\/ast.25.419"},{"key":"S1793351X1640002XBIB002","doi-asserted-by":"publisher","DOI":"10.1145\/2542205.2542206"},{"key":"S1793351X1640002XBIB003","doi-asserted-by":"publisher","DOI":"10.1561\/1500000042"},{"key":"S1793351X1640002XBIB005","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2008.916370"},{"key":"S1793351X1640002XBIB006","doi-asserted-by":"publisher","DOI":"10.1250\/ast.29.247"},{"key":"S1793351X1640002XBIB009","doi-asserted-by":"publisher","DOI":"10.1037\/0278-7393.11.1-4.629"},{"key":"S1793351X1640002XBIB011","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2010.2041386"},{"key":"S1793351X1640002XBIB012","first-page":"993","volume":"3","author":"Blei D. M.","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"S1793351X1640002XBIB016","doi-asserted-by":"publisher","DOI":"10.1016\/j.specom.2004.07.001"},{"key":"S1793351X1640002XBIB024","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. 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