# 30+ Years of Source Separation Research: Achievements and Future Challenges

arXiv: https://arxiv.org/abs/2501.11837 · HTML: https://mlsanity.com/paper/2501.11837/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2025.

## Built on by (most cited first, 7 of 7)

- [A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References](https://mlsanity.com/paper/2508.14623/) (2025)
- [MMAudioSep: Taming Video-to-Audio Generative Model Towards Video/Text-Queried Sound Separation](https://mlsanity.com/paper/2510.09065/) (2025)
- [Efficient and Fast Generative-Based Singing Voice Separation using a Latent Diffusion Model](https://mlsanity.com/paper/2511.20470/) (2025)
- [Generating Separated Singing Vocals Using a Diffusion Model Conditioned on Music Mixtures](https://mlsanity.com/paper/2511.21342/) (2025)
- [Predictive-Generative Drift Decomposition for Speech Enhancement and Separation](https://mlsanity.com/paper/2605.06189/) (2026)
- [TCPα: Margin-Controlled Confidence estimation for reliable Music Information Retrieval](https://mlsanity.com/paper/2608.20326/) (2026)
- [Predictive audio representations for early detection and tracking of hidden dynamic objects](https://mlsanity.com/paper/2609.13595/) (2026)

## Abstract (arXiv)

Source separation (SS) of acoustic signals is a research field that emerged in the mid-1990s and has flourished ever since. On the occasion of ICASSP's 50th anniversary, we review the major contributions and advancements in the past three decades in the speech, audio, and music SS research field. We will cover both single- and multi-channel SS approaches. We will also look back on key efforts to foster a culture of scientific evaluation in the research field, including challenges, performance metrics, and datasets. We will conclude by discussing current trends and future research directions.