Visualizing music and audio using self-similarity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Data mining: concepts and techniques
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SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
Misual: music visualization based on acoustic data
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Extraction and visualization of the repetitive structure of music in acoustic data: misual project
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
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Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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Journal of Visual Languages and Computing
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This paper proposes a method to identify and visualize repetitive structures in a pairwise representation of music to support people to imagine their affinity for music and the lightness of music intuitively, or in other words without listening to it. Repetitive structures in this paper are fragments that a music piece contains multiple times, and all these fragments may be slightly different but are perceived as very similar. For example, a tune might have little difference in tonality and could be performed by different kinds of musical instruments. We propose an algorithm to identify repetitive structures in a tune by using a self-similarity matrix. Identified structures are visualized on two kinds of images. One is a colored cylinder of varying diameter where colors represent repetitions and the diameter represents volume changes; the other is repetitions lines image, where different pairs of repetitions are shown on the Y-axis and the duration of each repeated pair is shown on the X-axis with a color. We selected eight tunes based on music psychology to evaluate the performance of the identification and visualization technique. Finally, we found that the amount of repetitions is related to the affinity for music, but not to the lightness of music. Volumes in both high-affinity music and high-lightness music change drastically.