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Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition

  • Ralph Abboud
  • Joe TekliEmail author
Methodologies and Application
  • 18 Downloads

Abstract

Over the past years, several approaches have been developed to create algorithmic music composers. Most existing solutions focus on composing music that appears theoretically correct or interesting to the listener. However, few methods have targeted sentiment-based music composition: generating music that expresses human emotions. The few existing methods are restricted in the spectrum of emotions they can express (usually to two dimensions: valence and arousal) as well as the level of sophistication of the music they compose (usually monophonic, following translation-based, predefined templates or heuristic textures). In this paper, we introduce a new algorithmic framework for autonomous music sentiment-based expression and composition, titled MUSEC, that perceives an extensible set of six primary human emotions (e.g., anger, fear, joy, love, sadness, and surprise) expressed by a MIDI musical file and then composes (creates) new polyphonic (pseudo) thematic, and diversified musical pieces that express these emotions. Unlike existing solutions, MUSEC is: (i) a hybrid crossover between supervised learning (SL, to learn sentiments from music) and evolutionary computation (for music composition, MC), where SL serves at the fitness function of MC to compose music that expresses target sentiments, (ii) extensible in the panel of emotions it can convey, producing pieces that reflect a target crisp sentiment (e.g., love) or a collection of fuzzy sentiments (e.g., 65% happy, 20% sad, and 15% angry), compared with crisp-only or two-dimensional (valence/arousal) sentiment models used in existing solutions, (iii) adopts the evolutionary-developmental model, using an extensive set of specially designed music-theoretic mutation operators (trille, staccato, repeat, compress, etc.), stochastically orchestrated to add atomic (individual chord-level) and thematic (chord pattern-level) variability to the composed polyphonic pieces, compared with traditional evolutionary solutions producing monophonic and non-thematic music. We conducted a large battery of tests to evaluate MUSEC’s effectiveness and efficiency in both sentiment analysis and composition. It was trained on a specially constructed set of 120 MIDI pieces, including 70 sentiment-annotated pieces: the first significant dataset of sentiment-labeled MIDI music made available online as a benchmark for future research in this area. Results are encouraging and highlight the potential of our approach in different application domains, ranging over music information retrieval, music composition, assistive music therapy, and emotional intelligence.

Keywords

Music sentiment analysis MIDI Evolutionary algorithms Algorithmic composition Supervised learning Fuzzy classification 

Notes

Acknowledgements

This study is partly funded by the National Council for Scientific Research - Lebanon (CNRS-L, grant number: NCSRLAU#887), by LAU (Grant Number: SOERC1516R003), as well as the Fulbright Visiting Scholar program (sponsored by the US Department of State, grant number: PS00232737). Special thanks go to music experts: Anthony Bou Fayad, Robert Lamah, and Joseph Khalifé, who helped evaluate the synthetic compositions, as well as Jean Marie Riachi for his participation in a live demonstration of the system. We would also like to thank the non-expert testers (including LAU students, faculty, staff, and friends) who volunteered to participate in the experimental evaluation.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Additional informed consent was obtained from all individual participants for whom identifying information is included in this article.

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Authors and Affiliations

  1. 1.E.C.E. Department, School of EngineeringLebanese American University (LAU), Byblos CampusByblosLebanon

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