# Mixture to Mixture: Leveraging Close-talk Mixtures as Weak-supervision for Speech Separation

arXiv: https://arxiv.org/abs/2402.09313 · HTML: https://mlsanity.com/paper/2402.09313/

## Key facts

- Cited by 5 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 14 Feb 2024.

## Built on by (most cited first, 5 of 5)

- [SuperM2M: Supervised and Mixture-to-Mixture Co-Learning for Speech Enhancement and Noise-Robust ASR](https://mlsanity.com/paper/2403.10271/) (2024)
- [ctPuLSE: Close-Talk, and Pseudo-Label Based Far-Field, Speech Enhancement](https://mlsanity.com/paper/2407.19485/) (2024)
- [Pseudo Labels-based Neural Speech Enhancement for the AVSR Task in the MISP-Meeting Challenge](https://mlsanity.com/paper/2505.24446/) (2025)
- [Mixture to Beamformed Mixture: Leveraging Beamformed Mixture as Weak-Supervision for Speech Enhancement and Noise-Robust ASR](https://mlsanity.com/paper/2507.15229/) (2025)
- [UltraM2M: Leveraging Text Transcripts and Mixture Constraints for Weakly-Supervised Speech Enhancement](https://mlsanity.com/paper/2610.05155/) (2026)

## Abstract (arXiv)

We propose mixture to mixture (M2M) training, a weakly-supervised neural speech separation algorithm that leverages close-talk mixtures as a weak supervision for training discriminative models to separate far-field mixtures. Our idea is that, for a target speaker, its close-talk mixture has a much higher signal-to-noise ratio (SNR) of the target speaker than any far-field mixtures, and hence could be utilized to design a weak supervision for separation. To realize this, at each training step we feed a far-field mixture to a deep neural network (DNN) to produce an intermediate estimate for each speaker, and, for each of considered close-talk and far-field microphones, we linearly filter the DNN estimates and optimize a loss so that the filtered estimates of all the speakers can sum up to the mixture captured by each of the considered microphones. Evaluation results on a 2-speaker separation task in simulated reverberant conditions show that M2M can effectively leverage close-talk mixtures as a weak supervision for separating far-field mixtures.