Type: Dataset
Tags: music, music transcription, midi, audio
Bibtex:
Tags: music, music transcription, midi, audio
Abstract:
URL: https://homes.cs.washington.edu/~thickstn/musicnet.html
License: No license specified, the work may be protected by copyright.
MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results.
URL: https://homes.cs.washington.edu/~thickstn/musicnet.html
License: No license specified, the work may be protected by copyright.
Bibtex:
@article{, title= {musicnet.tar.gz}, journal= {}, author= {John Thickstun and Zaid Harchaoui and Dean P. Foster and Sham M. Kakade}, year= {}, url= {https://homes.cs.washington.edu/~thickstn/musicnet.html}, abstract= {MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results. }, keywords= {music, music transcription, midi, audio}, terms= {}, license= {}, superseded= {} }