Input-Output HMM Applied to Automatic Arrangement for Guitars

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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.

著者

• Kameoka Hirokazu

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

• Hori Gen

  Faculty of Business Administration, Asia University

• Sagayama Shigeki

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

関連論文

• Input-Output HMM Applied to Automatic Arrangement for Guitars
• Input-Output HMM Applied to Automatic Arrangement for Guitars
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