Music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data

By M. Cuthbert, C. Ariza. International Society for Music Information Retrieval Conference, 2010

487 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Onsets and Frames: Dual-Objective Piano Transcription (2017, background): “Making a larger collection of symbolic music available also broadens the scope of possible computational musicology studies [8].”
DeepBach: a Steerable Model for Bach Chorales Generation (2016, methodology): “s local context. 2.4 Implementation Details We implemented DeepBach using Keras [9] with the Tensor ow [1] backend. We used the database of chorale harmonizations by J.S. Bach included in the music21 [11] toolkit. After removing chorales with instrumental parts and chorales containing parts with two”
Artsheets for Art Datasets (2021, background): “Under a broader historical lens, the JS Fake Chorales dataset reiterates environmental, sociopolitical, and professional factors that shaped Music21 toolkit [81].”
Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching (2016, background): “Both music21 (Cuthbert and Ariza, 2010) and melisma (Sleator and Temperley, 2001) include functionality for estimating chord sequences from symbolic data.”