{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T22:15:04Z","timestamp":1782339304444,"version":"3.54.5"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Music creation involves not only composing the different parts (e.g., melody, chords) of a musical work but also arranging\/selecting the instruments to play the different parts. While the former has received increasing attention, the latter has not been much investigated. This paper presents, to the best\nof our knowledge, the first deep learning models for rearranging music of arbitrary genres. Specifically, we build encoders and decoders that take a\npiece of polyphonic musical audio as input, and predict as output its musical score. We investigate disentanglement techniques such as adversarial\ntraining to separate latent factors that are related to the musical content (pitch) of different parts of the piece, and that are related to the instrumentation\n(timbre) of the parts per short-time segment. By disentangling pitch and timbre, our models have an idea of how each piece was composed and arranged. Moreover, the models can realize \u201ccomposition style transfer\u201d by rearranging a musical piece without much affecting its pitch content. We\nvalidate the effectiveness of the models by experiments on instrument activity detection and composition style transfer. To facilitate follow-up research,\nwe open source our code at https:\/\/github.com\/biboamy\/instrument-disentangle.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/652","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"4697-4703","source":"Crossref","is-referenced-by-count":22,"title":["Musical Composition Style Transfer via Disentangled Timbre Representations"],"prefix":"10.24963","author":[{"given":"Yun-Ning","family":"Hung","sequence":"first","affiliation":[{"name":"Research Center for IT Innovation, Academia Sinica, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I-Tung","family":"Chiang","sequence":"additional","affiliation":[{"name":"Research Center for IT Innovation, Academia Sinica, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-An","family":"Chen","sequence":"additional","affiliation":[{"name":"KKBOX Inc., Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Hsuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Research Center for IT Innovation, Academia Sinica, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:50:49Z","timestamp":1564300249000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/652"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/652","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}