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Introductory Studies on Raga Multi-track Music Generation of Indian classical music using AI

Authors: Gopi, Sreekanth; William, Femi;

Introductory Studies on Raga Multi-track Music Generation of Indian classical music using AI

Abstract

https://aimc2023.pubpub.org/pub/4pcwrxuo Recently, there has been an exponential expansion in research focusing on AI-based music generation. Our in- depth analysis of the arXiv dataset revealed a growing number of publications on this subject: over 273 AI music papers in the past two years, with 102 explicitly tackling AI music generation. However, one area that seems underrepresented is Indian traditional music. This study presents the application of artificial intelligence (AI) in creating Indian classical music, focusing on Raga-based music generation. We outline the two-stage music creation process, including the creative and technical aspects, and explore how AI can be integrated into these stages. We trained the models using the LSTM (Long Short-term Memory network) and Transformer models on the Dunya dataset, which includes almost 250 ragas played across 12 instruments. Further, the study proposes a new Raga Multi-Track Music Model (RMMM) model to generate multi-layered Raga-based music with enhanced authenticity and emotional resonance. Despite potential challenges, this research opens an exciting journey in AI-generated Indian classical music.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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