TL;DR
The Music Demixing Challenge 2021 aimed to advance music source separation by providing a diverse, professionally curated dataset and a transparent, accessible competition platform to evaluate and improve separation models.
Contribution
This paper introduces the MDX Challenge, featuring a new dataset, evaluation framework, and a broader, more diverse music genre inclusion to foster progress in music source separation.
Findings
Baseline models established for the challenge
Evaluation metrics for music demixing performance
Insights into challenges for future research
Abstract
Music source separation has been intensively studied in the last decade and tremendous progress with the advent of deep learning could be observed. Evaluation campaigns such as MIREX or SiSEC connected state-of-the-art models and corresponding papers, which can help researchers integrate the best practices into their models. In recent years, the widely used MUSDB18 dataset played an important role in measuring the performance of music source separation. While the dataset made a considerable contribution to the advancement of the field, it is also subject to several biases resulting from a focus on Western pop music and a limited number of mixing engineers being involved. To address these issues, we designed the Music Demixing (MDX) Challenge on a crowd-based machine learning competition platform where the task is to separate stereo songs into four instrument stems (Vocals, Drums, Bass,…
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