Music Demixing Challenge 2021

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 Challenge on a crowd-based machine learning competition platform where the task is to separate stereo songs into four instrument stems (Vocals, Drums, Bass, Other). The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i.e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers. In this paper, we provide the details of the datasets, baselines, evaluation metrics, evaluation results, and technical challenges for future competitions.

Hybrid Transformers for Music Source Separation

“…Finally, a recent trend has been to use both temporal and spectral domains, either through model blending, like KUIELAB-MDX-Net [17], or using a bi-U-Net structure with a shared backbone as Hybrid Demucs [2]. Hybrid Demucs was the first ranked architecture at the latest MDX MSS Competition [18], although it is now surpassed by Band-Split RNN. Using large datasets has been shown to be beneficial to the task of MSS.…”

Removing Distortion Effects in Music Using Deep Neural Networks

“…Despite the model not reaching the performance of today's source separation architectures [6], we included it in the evaluation due to its weak architectural priors.…”

“…unprocessed) source tracks are required. Given the recent advances in automatic mixing [4,5] and music source separation [6], a system could be developed that facilitates the adjustment of a stereo mixture to the user's taste and preferences similar to [7]. However, today's source separation systems are commonly trained on data that is based on music stems (e.g., MUSDB18 [8]).…”

Multi-Scale Temporal-Frequency Attention for Music Source Separation

“…The W and S indicates the waveform domain and spectrogram domain respectively. In the bottom half of Table 2, we also listed the results of the top-ranked hybrid domain systems in the Music Demixing (MDX) challenge at ISMIR 2021 [28], namely, KUIELab-MDX-Net [29] and Hybrid Demucs [30]. W + S indicates the method is working on hybrid domain.…”