# The Sound Demixing Challenge 2023 unicodex2013 Music Demixing Track

arXiv: https://arxiv.org/abs/2308.06979 · HTML: https://mlsanity.com/paper/2308.06979/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2023.

## Built on by (most cited first, 20 of 20)

- [Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling](https://mlsanity.com/paper/2504.15071/) (2025)
- [A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems](https://mlsanity.com/paper/2406.18747/) (2024)
- [Music Foundation Model as Generic Booster for Music Downstream Tasks](https://mlsanity.com/paper/2411.01135/) (2024)
- [Learning to Highlight Audio by Watching Movies](https://mlsanity.com/paper/2505.12154/) (2025)
- [Toward Deep Drum Source Separation](https://mlsanity.com/paper/2312.09663/) (2023)
- [SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation](https://mlsanity.com/paper/2409.10995/) (2024)
- [Score-informed Music Source Separation: Improving Synthetic-to-real Generalization in Classical Music](https://mlsanity.com/paper/2503.07352/) (2025)
- [Efficient and Fast Generative-Based Singing Voice Separation using a Latent Diffusion Model](https://mlsanity.com/paper/2511.20470/) (2025)
- [The first Cadenza challenges: using machine learning competitions to improve music for listeners with a hearing loss](https://mlsanity.com/paper/2409.05095/) (2024)
- [Training-Free Multi-Step Audio Source Separation](https://mlsanity.com/paper/2505.19534/) (2025)
- [The Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval](https://mlsanity.com/paper/2511.21247/) (2025)
- [Generating Separated Singing Vocals Using a Diffusion Model Conditioned on Music Mixtures](https://mlsanity.com/paper/2511.21342/) (2025)
- [SAM Audio Judge: A Unified Multimodal Framework for Perceptual Evaluation of Audio Separation](https://mlsanity.com/paper/2601.19702/) (2026)
- [VisualSpeech: Enhancing Prosody Modeling in TTS Using Video](https://mlsanity.com/paper/2501.19258/) (2025)
- [Source Separation of Small Classical Ensembles: Challenges and Opportunities](https://mlsanity.com/paper/2505.17823/) (2025)
- [Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions](https://mlsanity.com/paper/2508.14556/) (2025)
- [Source Separation for A Cappella Music](https://mlsanity.com/paper/2509.26580/) (2025)
- [Transcribing Rhythmic Patterns of the Guitar Track in Polyphonic Music](https://mlsanity.com/paper/2510.05756/) (2025)
- [The Costs of Reproducibility in Music Separation Research: a Replication of Band-Split RNN](https://mlsanity.com/paper/2603.09187/) (2026)
- [Music-Source-Separation-Training (MSST): A Unified Framework for Training and Evaluating Music Demixing Models](https://mlsanity.com/paper/2607.23395/) (2026)

## Abstract (arXiv)

This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX'23). We provide a summary of the challenge setup and introduce the task of robust music source separation (MSS), i.e., training MSS models in the presence of errors in the training data. We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding. We describe the methods that achieved the highest scores in the competition. Moreover, we present a direct comparison with the previous edition of the challenge (the Music Demixing Challenge 2021): the best performing system achieved an improvement of over 1.6dB in signal-to-distortion ratio over the winner of the previous competition, when evaluated on MDXDB21. Besides relying on the signal-to-distortion ratio as objective metric, we also performed a listening test with renowned producers and musicians to study the perceptual quality of the systems and report here the results. Finally, we provide our insights into the organization of the competition and our prospects for future editions.