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Exploring Music21 and Gensim for Music Data Analysis and Visualization

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Data Mining and Big Data (DMBD 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1071))

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Abstract

Computational musicology has been garnering attention since the 1950s. Musicologists appreciate the utilisation of computing power to look for patterns in music. The bottlenecks in the early days were attributed to the lack of standardization of computer representation of music and the lack of computing techniques specialized for the music domain. However, due to the increase in computing power, advances in music technology and machine learning techniques in recent years; the field of computational musicology has been revitalized. In this paper, we explored Music21 toolkit and Gensim, the recent open-source data analytical tool which includesd the Word2Vec model, for an analysis of Bach Chorales. The tools and techniques discussed in this paper have revealed many interesting exploratory fronts such as the semantic analogies of musical concepts which deserve a detailed investigation by computational musicologists.

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Notes

  1. 1.

    deepjazz: https://deepjazz.io/.

  2. 2.

    web.mit.edu/music21/doc/moduleReference/moduleCorpusChorales.html and www.jsbchorales.net.

  3. 3.

    The term prominent here is subjective. Here, we select chords that appear more than 75% from the major mode and the minor mode.

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Acknowledgments

We wish to thank anonymous reviewers for their comments that have helped improve this paper. We would like to thank the GSR office for their partial financial support given to this research.

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Authors and Affiliations

  1. Media Informatics Special Interest Group, Centre for Innovative Engineering, Universiti Teknologi Brunei, Gadong, Brunei

    Somnuk Phon-Amnuaisuk

  2. School of Computing and Informatics, Universiti Teknologi Brunei, Gadong, Brunei

    Somnuk Phon-Amnuaisuk

Authors
  1. Somnuk Phon-Amnuaisuk
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Correspondence to Somnuk Phon-Amnuaisuk .

Editor information

Editors and Affiliations

  1. Department of Machine Intelligence, Peking University, Beijing, China

    Ying Tan

  2. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Phon-Amnuaisuk, S. (2019). Exploring Music21 and Gensim for Music Data Analysis and Visualization. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2019. Communications in Computer and Information Science, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-32-9563-6_1

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  • DOI: https://doi.org/10.1007/978-981-32-9563-6_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9562-9

  • Online ISBN: 978-981-32-9563-6

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