Daniel Stoller, Mi Tian, Sebastian Ewert, and Simon Dixon
Automatic Music Transcription – An Overview
The capability of transcribing music audio into music notation is a fascinating example of human intelligence. It involves perception (analyzing complex auditory scenes), cognition (recognizing musical objects), knowledge representation (forming musical structures), and inference (testing alternative hypotheses). Automatic music transcription (AMT), i.e., the design of computational algorithms to convert acoustic music signals into some form of music notation, is a challenging task in signal processing and artificial intelligence. It comprises several subtasks, including multipitch estimation (MPE), onset and offset detection, instrument recognition, beat and rhythm tracking, interpretation of expressive timing and dynamics, and score typesetting.
Using a Neural Network Codec Approximation Loss to Improve Source Separation Performance in Limited Capacity Networks
Ishwarya Ananthabhotla, Sebastian Ewert, Joseph A. Paradiso
Rachel M Bittner, Magdalena Fuentes, David Rubinstein, Andreas Jansson, Keunwoo Choi, Thor Kell