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Title: Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models
Authors: Ojima, Yuta
Nakamura, Eita  kyouindb  KAKEN_id  orcid (unconfirmed)
Itoyama, Katsutoshi
Yoshii, Kazuyoshi  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 尾島, 優太
中村, 栄太
糸山, 克寿
吉井, 和佳
Keywords: Automatic Music Transcription
Chord Estimation
Non-negative Matrix Factorization
Bayesian Inference
Issue Date: 22-Nov-2018
Publisher: Cambridge University Press
Journal title: APSIPA Transactions on Signal and Information Processing
Volume: 7
Thesis number: e14
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.
Rights: © The Authors, 2018. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOI(Published Version): 10.1017/ATSIP.2018.17
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