Pitch-dependent identification of musical instrument sounds

Tetsuro Kitahara*, Masataka Goto, Hiroshi G. Okuno

*この研究の対応する著者

研究成果: Article査読

18 被引用数 (Scopus)

抄録

This paper describes a musical instrument identification method that takes into consideration the pitch dependency of timbres of musical instruments. The difficulty in musical instrument identification resides in the pitch dependency of musical instrument sounds, that is, acoustic features of most musical instruments vary according to the pitch (fundamental frequency, F0). To cope with this difficulty, we propose an F0-dependent multivariate normal distribution, where each element of the mean vector is represented by a function of F0. Our method first extracts 129 features (e.g., the spectral centroid, the gradient of the straight line approximating the power envelope) from a musical instrument sound and then reduces the dimensionality of the feature space into 18 dimension. In the 18-dimensional feature space, it calculates an F0-dependent mean function and an F0-normalized covariance, and finally applies the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments shows that the proposed method improved the recognition rate from 75.73% to 79.73%.

本文言語English
ページ(範囲)267-275
ページ数9
ジャーナルApplied Intelligence
23
3
DOI
出版ステータスPublished - 2005 12
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 人工知能

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