Input-Output HMM Applied to Automatic Arrangement for Guitars (Preprint)

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概要

• 論文の詳細を見る

• Given a relatively small selection of guitar scores for a large population of guitarists, there should be a certain demand for systems that can automatically arrange an arbitrary score for guitars. Our aim in this paper is to formulate the "fingering decision" and "arrangement" in a unified framework that can be cast as a decoding problem of a hidden Markov model (HMM). The left hand forms on the fingerboard are considered as the hidden states and the note sequence of a given score as an observed sequence generated by the HMM. Finding the most likely sequence of the hidden states thus corresponds to performing fingering decision or arrangement. The manual setting of HMM parameters reflecting preference of beginner guitarists lets the framework generate natural fingerings and arrangements suitable for beginners. Some examples of fingering and arrangement produced by the proposed method are presented.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.21(2013) No.2 (online)DOI http://dx.doi.org/10.2197/ipsjjip.21.264------------------------------

• 2013-04-15

著者

• Hirokazu Kameoka

  Graduate School of Information Science and Technologies, the University of Tokyo|NTT Communication Science Laboratories, NTT Corporation

• Gen Hori

  Faculty of Business Administration, Asia University

• Shigeki Sagayama

  Graduate School of Information Science and Technology, The University of Tokyo

関連論文

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• Dynamic Bayesian networks for symbolic polyhonic pitch modeling
• Designing Various Multivariate Analysis at Will via Generalized Pairwise Expression
• Input-Output HMM Applied to Automatic Arrangement for Guitars (Preprint)

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