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dc.contributor.author | Ojima, Yuta | en |
dc.contributor.author | Nakamura, Eita | en |
dc.contributor.author | Itoyama, Katsutoshi | en |
dc.contributor.author | Yoshii, Kazuyoshi | en |
dc.contributor.alternative | 尾島, 優太 | ja |
dc.contributor.alternative | 中村, 栄太 | ja |
dc.contributor.alternative | 糸山, 克寿 | ja |
dc.contributor.alternative | 吉井, 和佳 | ja |
dc.date.accessioned | 2018-12-04T05:13:31Z | - |
dc.date.available | 2018-12-04T05:13:31Z | - |
dc.date.issued | 2018-11-22 | - |
dc.identifier.issn | 2048-7703 | - |
dc.identifier.uri | http://hdl.handle.net/2433/235519 | - |
dc.description.abstract | This paper describes automatic music transcription with chord estimation for music audio signals. We focus on the fact that concurrent structures of musical notes such as chords form the basis of harmony and are considered for music composition. Since chords and musical notes are deeply linked with each other, we propose joint pitch and chord estimation based on a Bayesian hierarchical model that consists of an acoustic model representing the generative process of a spectrogram and a language model representing the generative process of a piano roll. The acoustic model is formulated as a variant of non-negative matrix factorization that has binary variables indicating a piano roll. The language model is formulated as a hidden Markov model that has chord labels as the latent variables and emits a piano roll. The sequential dependency of a piano roll can be represented in the language model. Both models are integrated through a piano roll in a hierarchical Bayesian manner. All the latent variables and parameters are estimated using Gibbs sampling. The experimental results showed the great potential of the proposed method for unified music transcription and grammar induction. | en |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Cambridge University Press | en |
dc.rights | © The Authors, 2018. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. | en |
dc.subject | Automatic Music Transcription | en |
dc.subject | Chord Estimation | en |
dc.subject | Non-negative Matrix Factorization | en |
dc.subject | Bayesian Inference | en |
dc.title | Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | APSIPA Transactions on Signal and Information Processing | en |
dc.identifier.volume | 7 | - |
dc.relation.doi | 10.1017/ATSIP.2018.17 | - |
dc.textversion | publisher | - |
dc.identifier.artnum | e14 | - |
dc.address | Kyoto University | en |
dc.address | Kyoto University | en |
dc.address | Kyoto University | en |
dc.address | Kyoto University | en |
dcterms.accessRights | open access | - |
datacite.awardNumber | 26700020 | - |
datacite.awardNumber | 16H01744 | - |
datacite.awardNumber | 16J05486 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName.alternative | Japan Society for the Promotion of Science (JSPS) | en |
jpcoar.funderName.alternative | Japan Society for the Promotion of Science (JSPS) | en |
jpcoar.funderName.alternative | Japan Society for the Promotion of Science (JSPS) | en |
出現コレクション: | 学術雑誌掲載論文等 |
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