Musical Source Separation: An Introduction
V. EVALUATION OF MUSICAL SOURCE SEPARATION MODELS
- DSD100: The Demixing Secrets Dataset 100 (DSD100) consists of a total of 100 full-track songs of different styles and includes the mixtures and four original sources/stems.
- MedleyDB: The MedleyDB is a dataset of 46 annotated, royalty-free multitrack recordings.
- MUSDB18: A description of the data set and the audio files used for the 2018 SiSEC Evaluation campaign.
- Others: A collection of data sets used for MSS.
Listening Tests Resources
- MUSHRAM: A Matlab interface for MUSHRA listening tests in Matlab.
- webMUSHRA: Web-based MUSHRA compliant to the ITU-R Recommendation BS.1534 (MUSHRA).
Evaluation Metrics Resources
- MUSEVAL: Evaluation Toolkit in Python for bss_eval v4.
- mir_eval.separation: Python implementation of bss_eval v3.
- PEASS: Matlab implementation of PEASS Toolkit.
SiSEC Evaluation Campaign
- MUS18: The community-based signal separation evaluation campaign offers a task for professionally-produced music recordings.
- Results SiSEC 2018: The full description of the results of the SiSEC 2018 Evaluation Campaign