Our technology will advance music rights management by facilitating the linking of each audio performance with its corresponding music work for better royalty distribution. It will also enable more refined music search, detecting new performances—live, for instance—of music compositions as well as varied interpretations of music standards. In addition, gaining access to and exerting control over the underlying music representation—the score, i. e., the music's DNA—unlocks far more sophisticated methods for manipulating and transforming the music.
muScribe: Automated tranScription of muSic
muScribe is poised to revolutionize music transcription by introducing advanced AI to transcribe audio recordings into detailed music scores. This project, carried out at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion at the University of Oslo, seeks to make music more accessible to the public.
Our primary objective is to develop a service for music archives to digitize music performance recording using state-of-the-art deep learning and our own cutting-edge research. Our hybrid approach is markedly original, merging the strengths of machine learning with symbolic AI, rooted in cognitive science and musicology.
The project is particularly oriented towards cultural institutions, music publishers, and copyright organizations. By automating transcription, we reduce costs and increase the precision and availability of music scores.
Published Oct. 21, 2024 12:00 PM
- Last modified Oct. 23, 2024 5:02 PM