Pitch-dependent identification of musical instrument sounds

Tetsuro Kitahara, Masataka Goto, Hiroshi G. Okuno

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

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%.

Original languageEnglish
Pages (from-to)267-275
Number of pages9
JournalApplied Intelligence
Volume23
Issue number3
DOIs
Publication statusPublished - 2005 Dec
Externally publishedYes

Keywords

  • Automatic music transcription
  • Computational auditory scene analysis
  • Fundamental frequency
  • Musical instrument identification
  • The pitch dependency

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence

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