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A Survey on Automatic Musical Chord Sequence Generation

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Advances in Power Systems and Energy Management (ETAEERE 2020, ETAEERE 2020)

Abstract

In this article, a sincere effort has been taken to review the developments on the Automatic Chord Sequence Generation methods. Literature regarding research in this area, worth noting over the past two decades are summarized describing the methods used. Various systems designed using neural networks, genetic algorithm, n-gram models, hidden Markov models etc. are considered for critical discussion. The technical value of the system models are studied rather than their entertainment value. In this paper, three simple prediction models for predicting the basic chord progressions involved in C major scale are developed and performances are compared. The future scopes of research and possible directions of improvements are also put forward.

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Correspondence to Abraham George .

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George, A., Mary, X.A. (2021). A Survey on Automatic Musical Chord Sequence Generation. In: Priyadarshi, N., Padmanaban, S., Ghadai, R.K., Panda, A.R., Patel, R. (eds) Advances in Power Systems and Energy Management. ETAEERE ETAEERE 2020 2020. Lecture Notes in Electrical Engineering, vol 690. Springer, Singapore. https://doi.org/10.1007/978-981-15-7504-4_2

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  • DOI: https://doi.org/10.1007/978-981-15-7504-4_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7503-7

  • Online ISBN: 978-981-15-7504-4

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