Automatic Music Transcription: An Overview
By Emmanouil Benetos, Simon E. Dixon, Zhiyao Duan, Sebastian Ewert. IEEE Signal Processing Magazine, 2019
295 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/b0a4c24d1bc96d71402fc8668a823c43d8bc47dc
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Citations in the corpus
- Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions (2020, methodology): “ormationretrieval(MIR) techniques such as downbeat tracking [2, 3, 16] and chord recognition [17, 35], which differentiates it from existing approaches. Moreover, we use automatic music transcription [1, 21, 25] to prepare the MIDI data, so the model learns from expressive piano performances. We sho”
- Pop Music Transformer: Generating Music with Rhythm and Harmony (2020, background): “, 2018], a score to be converted into events can be either a MIDI score with no expressive dynamics and timing, or a MIDI performance that has been converted from an expressive audio recording by means such as a MIDI keyboard, or automatic music transcription [Benetos et al., 2018].”