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

The essence of music is the "expression" of each performer, namely, the deviations in amplitude, pitch, tim-bre and tempo/rhythm that they add to their performance.Hence, in computational applications of music, deviationanalysis is important. However, because the musical toneof excitation-continuous musical instruments changes com-plexly in accordance with the level of controllability, it isdifficult to analyze the deviations. This paper proposes fivedeviation analysis methods, focused on statistical consis-tency and repetition, for bowed string instruments. Resultsof analysis using each of the methods are presented. By us-ing CMKLD, which is an acoustic feature based on auralcharacteristics, the error rate of musical score alignment wasdecreased to 63.2 percentage points. By modeling the soundcontrol 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. Byusing a generative model of amplitude contour, focused onstatistical consistency, a performer\u27s phrasing and variationof articulation could be analyzed. By using a physical modelof a violin in the frequency domain, high-quality sound couldbe synthesized via quantitative expressiveness parameters.By removing deviations that have no statistical consistency,misplayed sounds could be adjusted. These results show thatthe proposed methods can be used to analyze the expressivedeviation of bowed string instruments, and can be appliedto musical information processing systems such as automaticperformance and automatic skill evaluation