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
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Please refer to the Github repository for results not plotted here.
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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.
References
Bhagyalekshmy, S.: Ragas in Carnatic Music. CBHH Publications, Trivandrum (1990)
Boot, P., Volk, A., Bas de Haas, W.: Evaluating the role of repeated patterns in folk song classification and compression. J. New Music Res. 45(3), 223–238 (2016)
Cambouropoulos, E.: Musical parallelism and melodic segmentation: a computational approach. Music. Percept. 23(3), 249–268 (2006)
Conklin, D., Anagnostopoulou, C.: Segmental pattern discovery in music. INFORMS J. Comput. 18(3), 285–293 (2006)
Dannenberg, R.B.: Pattern discovery techniques for music audio. J. New Music Res. 32, 153–163 (2003)
Dannenberg, R.B., Hu, N.: Discovering musical structure in audio recordings. In: Anagnostopoulou, C., Ferrand, M., Smaill, A. (eds.) ICMAI 2002. LNCS (LNAI), vol. 2445, pp. 43–57. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45722-4_6
Discovery of Repeated Themes & Sections - MIREX Wiki (2017). https://www.music-ir.org/mirex/wiki/2017:%20Discovery_of_Repeated_Themes_%26_Sections
Foote, J., Cooper, M., Nam, U.: Audio retrieval by rhythmic similarity. In: Proceedings of the 3rd International Society for Music Information Retrieval Conference (2002)
Forth, J.: Cognitively-motivated geometric methods of pattern discovery and models of similarity in music. Ph.D. thesis, Goldsmiths, University of London (2012)
Fuentes, M., et al.: mirdata vol. 0.3.0. Zenodo (2021). https://doi.org/10.5281/zenodo.4355859
Ganguli, K., Gulati, S., Serra, X., Rao, P.: Data-driven exploration of melodic structure in Hindustani music. In: Proceedings of the 17th International Society for Music Information Retrieval Conference (2016)
Gjerdingen, R.: Music in the Galant style. OUP USA (2007)
Gulati, S., Serra, J., Ganguli, K.K., Serra, X.: Landmark detection in Hindustani music melodies. In: International Computer Music Conference Proceedings (2014)
Gulati, S., Serrá, J., Ishwar, V., Serra, X.: Mining melodic patterns in large audio collections of Indian art music. In: International Conference on Signal Image Technology and Internet Based Systems (SITIS-MIRA), pp. 264–271. Morocco, 9, 87, 124, 148 (2014)
Gulati, S., Serrá, J., Serra, X.: Improving melodic similarity in Indian art music using culture-specific melodic characteristics. In: Proceedings of the 16th International Society for Music Information Retrieval Conference (2015)
Gulati, S., Serrà, J., Ganguli, K., Sertan, Ş., Serra, X.: Time-delayed melody surfaces for Raga recognition. In: Proceedings of the 17th International Society for Music Information Retrieval Conference, pp. 751–757 (2016)
Gulati, S.: Computational approaches for melodic description in Indian art music corpora. Ph.D. thesis, Universitat Pompeu Fabra, Barcelona (2016)
Hennequin, R., Khlif, A., Voituret, F., Moussallam, M.: Spleeter: a fast and state-of-the-art music source separation tool with pre-trained models. J. Open Source Softw. 5(50), 2154 (2019)
Janssen, B., de Haas, W.B., Volk, A., van Kranenburg, P.: Finding repeated patterns in music: state of knowledge, challenges, perspectives. In: Aramaki, M., Derrien, O., Kronland-Martinet, R., Ystad, S. (eds.) CMMR 2013. LNCS, vol. 8905, pp. 277–297. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12976-1_18
Klapuri, A.: Pattern induction and matching in music signals. In: 7th International Symposium on Exploring Music Contents, CMMR, Málaga, Spain, pp. 188–204 (2010)
Krebs, F., Böck, S. Widmer, G.: Rhythmic pattern modeling for beat and downbeat tracking in musical audio. In: Proceedings of the 14th International Society for Music Information Retrieval Conferences (2013)
Lie, L., Wang, M., Zhang, H.: Repeating pattern discovery and structure analysis from acoustic music data. In: Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR, pp. 275–282 (2004)
Murthy, H., Bellur, A.: Motif spotting in an Alapana in Carnatic music. In: Proceedings of the 14th International Society for Music Information Retrieval Conferences (2013)
Nuttall, T., Casado, M.C., Ferraro, A., Conklin, D., Caro Repetto, R.: A computational exploration of melodic patterns in Arab-Andalusian music. J. Math. Music 1–13 (2021)
Pearson, L.: Coarticulation and gesture: an analysis of melodic movement in South Indian raga performance. Music. Anal. 35(3), 280–313 (2016)
Rao, P., Ross, J.C., Ganguli, K.K.: Distinguishing raga-specific intonation of phrases with audio analysis. Ninaad 26–27(1), 59–68 (2013)
Rao, P., et al.: Classification of melodic motifs in raga music with time-series matching. J. New Music Res. 43, 115–131 (2014)
Ren, I.Y.: Closed patterns in folk music and other genres. In: Proceedings of the 6th International Workshop on Folk Music Analysis, FMA, pp. 56–58 (2016)
Ren, I.Y., Volk, A., Swierstra, W., Veltkamp, R.C.: In search of the consensus among musical pattern discovery algorithms. In: Proceedings of the 18th International Society for Music Information Retrieval ISMIR, pp. 671–680 (2017)
Ren, I.Y., Volk, A., Swierstra, W., Veltkamp, R.C.: A computational evaluation of musical pattern discovery algorithms. CoRR (2020)
Salamon, J., Gomez, E.: Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Trans. Audio Speech Lang. Process. 20, 1759–1770 (2012)
Srinivasamurthy, A., Gulati, S., Caro Repetto, R., Serra, X.: Saraga: open dataset for research on Indian Art Music. Empir. Musicol. Rev. (2020). https://compmusic.upf.edu/ [Preprint]
Thomas, M., Murthy, Y.S., Koolagudi, S.G.: Detection of largest possible repeated patterns in Indian audio songs using spectral features. In: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–5 (2016)
Volk, A., van Kranenburg, P.: Melodic similarity among folk songs: an annotation study on similarity based categorization in music. Music. Sci. 16(3), 317–339 (2012)
Wang, C., Hsu, J., Dubnov, S.: Music pattern discovery with variable Markov oracle: a unified approach to symbolic and audio representations. In: Proceedings of the 16th International Society for Music Information Retrieval Conference, pp. 176–182 (2015)
Yeh, C.M., et al.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: IEEE 16th International Conference on Data Mining (ICDM), pp. 1317–1322 (2016)
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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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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