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AI-Driven Music Composition by Integrating RNNs and GAs for Personalized Pop Songs
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Author(s): K. S. Jishnu (SRM Institute of Science and Technology, Kattankulathur, India), P. S. Shijukumar (SRM Institute of Science and Technology, India), G. Bhargavi (SRM Institute of Science and Technology, India), Vimal Sankar (TKM Institute of Technology, India), P. S. Sujith Kumar (Sree Buddha College of Engineering, Pattoor, India)and Nisha Thorakattu Madathil (United Arab Emirates University, UAE)
Copyright: 2026
Pages: 30
Source title:
Artificial Intelligence in Music Production: Innovations, Practices, and Industry Implications
Source Author(s)/Editor(s): Randy Joy Magno Ventayen (Pangasinan State University, Phillippines)
DOI: 10.4018/979-8-3373-6279-3.ch006
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Abstract
An AI-powered music composition framework is introduced, capable of generating complete pop songs by integrating Recurrent Neural Networks (RNNs) with Genetic Algorithms (GAs). RNNs produce melodically coherent MIDI sequences that reflect long-term musical dependencies, while GAs refine song structure based on user preferences such as variation, transition smoothness, and catchiness. Unlike the MAGMA framework, which is limited to instrumental MIDI creation, the proposed system incorporates vocals using a realistic Text-to-Speech (TTS) engine. Lyrics can be user provided or generated through NLP and are precisely synchronized with the MIDI output using tools such as librosa, pydub, and fluidsynth. The implementation uses Python libraries including tensorflow, torch, pretty-midi, music21, pandas, and matplotlib, and a feedback loop allows users to iteratively improve song quality. The framework aims to make high-quality, personalized music production accessible to musicians, content creators, and professionals.
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