{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T04:51:38Z","timestamp":1777956698324,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100007691","name":"Universidade da Beira Interior","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007691","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Synthesising appropriate choreographies from music remains an open problem, due to the need to align musical semantics with subjective human motions. We introduce MDLT, a novel approach that frames the choreography generation problem as a translation task. Our method leverages the AIST++ and PhantomDance data sets to learn to translate sequences of audio into corresponding dance poses. We present two variants of MDLT: MDLT-T based on the Transformer architecture, and MDLT-M based on the Mamba architecture, for strong long-horizon sequence modeling. Trained on these data sets, our method enables a robotic arm to dance, and can generalize to humanoid robots through its architecture-agnostic design. Evaluation metrics, including Average Joint Error and Fr\u00e9chet Inception Distance, consistently demonstrate that, when given a piece of music, MDLT excels at producing realistic and high-quality choreography. The code will be made available upon acceptance.<\/jats:p>","DOI":"10.1007\/s00521-026-11905-7","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T03:38:21Z","timestamp":1777952301000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Music to dance as language translation using sequence models"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5901-1802","authenticated-orcid":false,"given":"Andr\u00e9","family":"Correia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu\u00eds A.","family":"Alexandre","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"key":"11905_CR1","doi-asserted-by":"crossref","unstructured":"Li R, Yang S, Ross D, Kanazawa A (2021) AI choreographer: music conditioned 3d dance generation with aist++. 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