Abstract: The art of music transcription, transforming fleeting audio recordings into the permanence of sheet music, holds immense potential for musicians, educators, and historical preservationists. This project embarks on an exploration of Recurrent Neural Networks (RNNs) as a powerful tool for automated music transcription. The focus here is on meticulously converting MP3 audio files into MIDI files, subsequently translating them into comprehensive and expressive musical notations.
The proposed RNN model aspires to achieve groundbreaking accuracy in capturing the very essence of music – pitch, rhythm, and duration – directly from audio recordings. This feat, if achieved, would transcend mere note recognition and delve into the heart of what makes music so captivating. By effectively translating the intricate language of audio into the symbolic language of musical notation, the model paves the way for a more profound understanding and appreciation of music.
Keywords: Music notes classification,Artificial intelligence,Deep learning,Musical Transcription ,Frequency based analysis, Machine learning,Pitch identification.
Cite:
Harshitha, Prabhanjan Hippargi, Shobith R Acharya, Shreya S Poojary, Ms. Amrutha, "Automatic Music Transcription To Music Notes Using Artificial Intelligence", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13361.
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DOI:
10.17148/IJARCCE.2024.13361
