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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM50461

Melody AI – An AI Music Generator

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM50461

Volume:

9 |

6 |

Jun 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem50461

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KAK

Keetha Ajay Kumar

JSK

Jalli Srujan Kumar

GP

Gotte Pavani

KSA

Kalva Sai Abhinav

EF

Elijah Francis

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Abstract

– MELODY AI is an innovative system designed to generate music using artificial intelligence by transforming textual descriptions into original compositions. Leveraging the MusicGen model from Hugging Face, the system employs advanced deep learning techniques to interpret user input and produce corresponding audio tracks. The workflow includes interpreting the user’s prompt, translating it into a musical representation, and synthesizing it into audio form. The AI model has been fine-tuned to maintain musical richness and structural integrity, enabling it to generate expressive, genre-diverse compositions. The system effectively captures the essence of user prompts, delivering melodically coherent and emotionally resonant music. Its ability to generate tailored and aesthetically pleasing music makes it a valuable tool for creators in film, digital media, and the arts. This project highlights the growing role of AI in reshaping music composition and enhancing creative expression.

KEYWORDS

AI-generated music deep learning text-to-audio synthesis Hugging Face MusicGen creative AI tools

Cite This Article

Keetha Ajay Kumar, Jalli Srujan Kumar, Gotte Pavani, Kalva Sai Abhinav, Elijah Francis, "Melody AI – An AI Music Generator," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 6, Jun 2025. https://doi.org/10.55041/ijsrem50461

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Publication Date

6/15/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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