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SyVMO: Synchronous Variable Markov Oracle for Modeling and Predicting Multi-part Musical Structures

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Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12693))

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Abstract

We present SyVMO, an algorithmic extension of the Variable Markov Oracle algorithm, to model and predict multi-part dependencies from symbolic music manifestations. Our model has been implemented as a software application named INCITe for computer-assisted algorithmic composition. It learns variable amounts of musical data from style-agnostic music represented as multiple viewpoints. To evaluate the SyVMO model within INCITe, we adopted the Creative Support Index survey and semi-structured interviews. Four expert composers participated in the evaluation using both personal and exogenous music corpus of variable size. The results suggest that INCITe shows great potential to support creative music tasks, namely in assisting the composition process. The use of SyVMO allowed the creation of polyphonic music suggestions from style-agnostic sources while maintaining a coherent melodic structure.

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Notes

  1. 1.

    A notable exception to this case is counterpoint modeling and generation [13], where alphabet events typically encode vertical pitch aggregates when processing polyphonic music structures.

  2. 2.

    Efficient data structure for representing substring index of a given string which allows for storage, processing, and retrieval of compressed information about all its substrings.

  3. 3.

    The implementation for both SyVMO and INCITe can be found online at https://github.com/NadiaCarvalho/INCITe.git, last access on February 2021.

  4. 4.

    https://musescore.org/en, last access on February 2021.

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Acknowledgements

Research partially funded by the project “Co-POEM:Platform for the Collaborative Generation of European Popular Music” (ES01-KA201-064933) which has been funded with support from the European Commission and the project “Experimentation in music in Portuguese culture: History, contexts and practices in the 20th and 21st centuries” (POCI-01-0145-FEDER-031380) co-funded by the European Union through the Operational Program Competitiveness and Internationalization, in its ERDF component, and by national funds, through the Portuguese Foundation for Science and Technology; and by the European Union’s Horizon 2020 research. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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Authors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, Portugal

    Nádia Carvalho & Gilberto Bernardes

  2. INESC TEC, Porto, Portugal

    Gilberto Bernardes

Authors
  1. Nádia Carvalho
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  2. Gilberto Bernardes
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Correspondence to Nádia Carvalho or Gilberto Bernardes .

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Editors and Affiliations

  1. University of A Coruña, A Coruña, Spain

    Juan Romero

  2. University of Coimbra, Coimbra, Portugal

    Tiago Martins

  3. University of A Coruña, A Coruña, Spain

    Nereida Rodríguez-Fernández

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Carvalho, N., Bernardes, G. (2021). SyVMO: Synchronous Variable Markov Oracle for Modeling and Predicting Multi-part Musical Structures. In: Romero, J., Martins, T., Rodríguez-Fernández, N. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2021. Lecture Notes in Computer Science(), vol 12693. Springer, Cham. https://doi.org/10.1007/978-3-030-72914-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-72914-1_3

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-72914-1

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