A Multi-modal Platform for Semantic Music Analysis: Visualizing Audio-and Score-Based Tension

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Conference Proceeding

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Musicologists, music cognition scientists and others have long studied music in all of its facets. During the last few decades, research in both score and audio technology has opened the doors for automated, or (in many cases) semi-automated analysis. There remains a big gap, however, between the field of audio (performance) and score-based systems. In this research, we propose a web-based Interactive system for Multi-modal Music Analysis (IMMA), that provides musicologists with an intuitive interface for a joint analysis of performance and score. As an initial use-case, we implemented a tension analysis module in the system. Tension is a semantic characteristic of music that directly shapes the music experience and thus forms a crucial topic for researchers in musicology and music cognition. The module includes methods for calculating tonal tension (from the score) and timbral tension (from the performance). An audio-to-score alignment algorithm based on dynamic time warping was implemented to automate the synchronization between the audio and score analysis. The resulting system was tested on three performances (violin, flute, and guitar) of Paganini's Caprice No. 24 and four piano performances of Beethoven's Moonlight Sonata. We statistically analyzed the results of tonal and timbral tension and found correlations between them. A clustering algorithm was implemented to find segments of music (both within and between performances) with similar shape in their tension curve. These similar segments are visualized in IMMA. By displaying selected audio and score characteristics together with musical score following in sync with the performance playback, IMMA offers a user-friendly intuitive interface to bridge the gap between audio and score analysis.

Publication Title

Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017

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