Songs2Notes : ML Model Converting Songs to Music Notes

This project introduces a versatile machine learning model for converting MIDI files into detailed musical notation, adaptable across diverse instruments and genres. Unlike existing tools with limited scope, our system enables users to choose from a wide range of instruments, offering accurate, context-specific transcriptions. By leveraging advanced audio processing and deep learning techniques, the model extracts core features like pitch, mapping them to precise notations tailored to the selected instrument. Trained on an extensive dataset covering various songs and scores, the model accounts for tonal qualities and playing styles unique to each instrument. Its strengths lie in handling polyphonic textures, fluctuating tempos, and multiple genres, making it invaluable for musicians, composers, and educators. The model's scalability ensures continuous adaptability to emerging musical forms, providing a robust solution for automatic transcription.

Authors

Publication Details

Published
2025-02-21
DOI
https://doi.org/10.1109/icitiit64777.2025.11040432
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
0.00
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article

Songs2Notes : ML Model Converting Songs to Music Notes

Neha Lilly Kishor, Afreen Anil, Neha S Eluvathingal, Saran KS et al.
Music and Audio Processing
article

Songs2Notes : ML Model Converting Songs to Music Notes

Neha Lilly Kishor, Afreen Anil, Neha S Eluvathingal, Saran KS, Ajith Jacob
article en

Abstract

This project introduces a versatile machine learning model for converting MIDI files into detailed musical notation, adaptable across diverse instruments and genres. Unlike existing tools with limited scope, our system enables users to choose from a wide range of instruments, offering accurate, context-specific transcriptions. By leveraging advanced audio processing and deep learning techniques, the model extracts core features like pitch, mapping them to precise notations tailored to the selected instrument. Trained on an extensive dataset covering various songs and scores, the model accounts for tonal qualities and playing styles unique to each instrument. Its strengths lie in handling polyphonic textures, fluctuating tempos, and multiple genres, making it invaluable for musicians, composers, and educators. The model's scalability ensures continuous adaptability to emerging musical forms, providing a robust solution for automatic transcription.

Gender equality
Openalex Percentile: Top 82%
Music and Audio Processing
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