of analysis using each of the methods are presented. By us

(1)

Music Signal Analysis Methods and Their Applications to Bowed String Instruments Based on Expressiveness Modeling

Yuma Koizumi

Graduate School of Computer and Information Sciences, Hosei University EJ-mail: [email protected]

Abstract

The essence of music is the "expression" of each per former, namely, the deviations in amplitude, pitch, tim bre and tempo/rhythm that they add to their performance.

Hence, in computational applications of music, deviation analysis is important. However, because the musical tone

of excitation-continuous musical instruments changes com plexly in accordance with the level of controllability, it is difficult to analyze the deviations. This paper proposes five

deviation analysis methods, focused on statistical consis tency and repetition, for bowed string instruments. Results

of analysis using each of the methods are presented. By us

ing CMKLD, which is an acoustic feature based on aural

characteristics, the error rate of musical score alignment was decreased to 63.2 percentage points. By modeUng the sound

control indeterminacy due to performance expression, the er

ror rate of intra-note segmentation was decreased to 89.4 (A- to-S) and 48.8 (S-to-R) percentage points, respectively. By

using a generative model of amplitude contour, focused on statistical consistency, a performer's phrasing and variation of articulation could be analyzed. By using a physical model of a violin in the frequency domain, high-quality sound could be synthesized via quantitative expressiveness parameters.

By removing deviations that have no statistical consistency,

misplayed sounds could be adjusted. These results show that

the proposed methods can be used to analyze the expressive deviation of bowed string instruments, and can be applied

to musical information processing systems such as automatic performance and automatic skill evaluation.

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2014. (in pres.s).

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FIT-2()13. 201.3.

{(>) "Eximt.ssive Oriennxl Time-Scale Adjustment for Mis-playod Mu sical Signals based on Tempo Curve Estimation" DAFx-16. 2013.