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Generating Musical Compositions through a Data-Driven Approach along with Static Implementations of Theoretical Principles

  • In the field of automatic music generation, one of the greatest challenges is the consistent generation of pieces continuously perceived positively by the majority of the audience since there is no objective method to determine the quality of a musical composition. However, composing principles, which have been refined for millennia, have shaped the core characteristics of today's music. A hybrid music generation system, mlmusic, that incorporates various static, music-theory-based methods, as well as data-driven, subsystems, is implemented to automatically generate pieces considered acceptable by the average listener. Initially, a MIDI dataset, consisting of over 100 hand-picked pieces of various styles and complexities, is analysed using basic music theory principles, and the abstracted information is fed into explicitly constrained LSTM networks. For chord progressions, each individual network is specifically trained on a given sequence length, while phrases are created by consecutively predicting the notes' offset, pitch and duration. Using these outputs as a composition's foundation, additional musical elements, along with constrained recurrent rhythmic and tonal patterns, are statically generated. Although no survey regarding the pieces' reception could be carried out, the successful generation of numerous compositions of varying complexities suggests that the integration of these fundamentally distinctive approaches might lead to success in other branches.

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Metadaten
Document Type:Report
Language:English
Author:Daniel Jiang
Number of pages:viii, 62
ISBN:978-3-96043-103-9
ISSN:1869-5272
URN:urn:nbn:de:hbz:1044-opus-65461
DOI:https://doi.org/10.18418/978-3-96043-103-9
Contributor:Wolfgang Heiden
Supervisor:Wolfgang Heiden, Ernst Kruijff
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2022/12/15
Series (Volume):Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences. Department of Computer Science (04-2022)
Keyword:LSTM; Machine Learning; automatic music generation; hybrid system; music analysis
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Series:Technical Report / University of Applied Sciences Bonn-Rhein-Sieg. Department of Computer Science
Entry in this database:2022/12/15
Licence (Multiple languages):License LogoIn Copyright - Educational Use Permitted (Urheberrechtsschutz - Nutzung zu Bildungszwecken erlaubt)