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The Matrix Profile for Motif Discovery in Audio - An Example Application in Carnatic Music

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Music in the AI Era (CMMR 2021)

Abstract

We present here a pipeline for the automated discovery of repeated motifs in audio. Our approach relies on state-of-the-art source separation, predominant pitch extraction and time series motif detection via the matrix profile. Owing to the appropriateness of this approach for the task of motif recognition in the Carnatic musical style of South India, and with access to the recently released Saraga Dataset of Indian Art Music, we provide an example application on a recording of a performance in the Carnatic rāga, Rītigauḷa, finding 56 distinct patterns of varying lengths that occur at least 3 times in the recording. The authors include a discussion of the potential musicological significance of this motif finding approach in relation to the particular tradition and beyond.

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Notes

  1. 1.

    https://github.com/thomasgnuttall/carnatic-motifs-cmmr-2021/.

  2. 2.

    https://compmusic.upf.edu/.

  3. 3.

    https://musicbrainz.org/recording/5fa0bcfd-c71e-4d6f-940e-0cef6fbc2a32.

  4. 4.

    Please refer to the Github repository for results not plotted here.

  5. 5.

    Although some motifs are annotated in the Saraga dataset, these annotations are not complete. Such annotating is extremely time consuming and must be done by practitioners of the style.

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Acknowledgments

This research was funded by the MUSICAL AI project (PID2019-111403GB-I00) granted by the Ministry of Science and Innovation of the Spanish Government. We also thank Rafael Caro Repetto for his continued guidance and input.

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Correspondence to Thomas Nuttall .

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Nuttall, T., Plaja-Roglans, G., Pearson, L., Serra, X. (2023). The Matrix Profile for Motif Discovery in Audio - An Example Application in Carnatic Music. In: Aramaki, M., Hirata, K., Kitahara, T., Kronland-Martinet, R., Ystad, S. (eds) Music in the AI Era. CMMR 2021. Lecture Notes in Computer Science, vol 13770 . Springer, Cham. https://doi.org/10.1007/978-3-031-35382-6_18

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  • DOI: https://doi.org/10.1007/978-3-031-35382-6_18

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