NeuralPMG: A Neural Polyphonic Music Generation System Based on Machine Learning Algorithms Article Swipe
Tommaso Colafiglio
,
Carmelo Ardito
,
Paolo Sorino
,
Domenico Lofù
,
Fabrizio Festa
,
Tommaso Di Noia
,
Eugenio Di Sciascio
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s12559-024-10280-6
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s12559-024-10280-6
The realm of music composition, augmented by technological advancements such as computers and related equipment, has undergone significant evolution since the 1970s. In the field algorithmic composition, however, the incorporation of artificial intelligence (AI) in sound generation and combination has been limited. Existing approaches predominantly emphasize sound synthesis techniques, with no music composition systems currently employing Nicolas Slonimsky’s theoretical framework. This article introduce NeuralPMG , a computer-assisted polyphonic music generation framework based on a Leap Motion (LM) device, machine learning (ML) algorithms, and brain-computer interface (BCI). ML algorithms are employed to classify user’s mental states into two categories: focused and relaxed. Interaction with the LM device allows users to define a melodic pattern, which is elaborated in conjunction with the user’s mental state as detected by the BCI to generate polyphonic music. NeuralPMG was evaluated through a user study that involved 19 students of Electronic Music Laboratory at a music conservatory, all of whom are active in the music composition field. The study encompassed a comprehensive analysis of participant interaction with NeuralPMG. The compositions they created during the study were also evaluated by two domain experts who addressed their aesthetics, innovativeness, elaboration level, practical applicability, and emotional impact. The findings indicate that NeuralPMG represents a promising tool, offering a simplified and expedited approach to music composition, and thus represents a valuable contribution to the field of algorithmic music composition.
Related Topics
Concepts
Polyphony
Computer science
Melody
Computer music
Musical composition
Composition (language)
Field (mathematics)
Realm
Brain–computer interface
Artificial intelligence
Music information retrieval
Pop music automation
Human–computer interaction
Algorithm
Machine learning
Musical
Psychology
Mathematics
Visual arts
Law
Political science
Art
Linguistics
Psychiatry
Pedagogy
Philosophy
Pure mathematics
Electroencephalography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s12559-024-10280-6
- https://link.springer.com/content/pdf/10.1007/s12559-024-10280-6.pdf
- OA Status
- hybrid
- Cited By
- 13
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396929191
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396929191Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s12559-024-10280-6Digital Object Identifier
- Title
-
NeuralPMG: A Neural Polyphonic Music Generation System Based on Machine Learning AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-15Full publication date if available
- Authors
-
Tommaso Colafiglio, Carmelo Ardito, Paolo Sorino, Domenico Lofù, Fabrizio Festa, Tommaso Di Noia, Eugenio Di SciascioList of authors in order
- Landing page
-
https://doi.org/10.1007/s12559-024-10280-6Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s12559-024-10280-6.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s12559-024-10280-6.pdfDirect OA link when available
- Concepts
-
Polyphony, Computer science, Melody, Computer music, Musical composition, Composition (language), Field (mathematics), Realm, Brain–computer interface, Artificial intelligence, Music information retrieval, Pop music automation, Human–computer interaction, Algorithm, Machine learning, Musical, Psychology, Mathematics, Visual arts, Law, Political science, Art, Linguistics, Psychiatry, Pedagogy, Philosophy, Pure mathematics, ElectroencephalographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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