# MixCycle: Unsupervised Speech Separation via Cyclic Mixture Permutation Invariant Training

arXiv: https://arxiv.org/abs/2202.03875 · HTML: https://mlsanity.com/paper/2202.03875/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 27 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Attention is All You Need in Speech Separation](https://mlsanity.com/paper/2010.13154/) (2020)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks](https://mlsanity.com/paper/1703.06284/) (2017)
- [Wavesplit: End-to-End Speech Separation by Speaker Clustering](https://mlsanity.com/paper/2002.08933/) (2020)
- [Sudo rm -rf: Efficient Networks for Universal Audio Source Separation](https://mlsanity.com/paper/2007.06833/) (2020)
- [Voice Separation with an Unknown Number of Multiple Speakers](https://mlsanity.com/paper/2003.01531/) (2020)
- [Differentiable Consistency Constraints for Improved Deep Speech Enhancement](https://mlsanity.com/paper/1811.08521/) (2018)
- [Unsupervised training of a deep clustering model for multichannel blind source separation](https://mlsanity.com/paper/1904.01340/) (2019)
- [Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision](https://mlsanity.com/paper/1911.02182/) (2019)
- [Sparse, Efficient, and Semantic Mixture Invariant Training: Taming In-the-Wild Unsupervised Sound Separation](https://mlsanity.com/paper/2106.00847/) (2021)
- [Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation](https://mlsanity.com/paper/2106.07843/) (2021)
- [Mixup-breakdown: a consistency training method for improving generalization of speech separation models](https://mlsanity.com/paper/1910.13253/) (2019)
- [Adapting Speech Separation to Real-World Meetings Using Mixture Invariant Training](https://mlsanity.com/paper/2110.10739/) (2021)
- [Audio Source Separation Using Variational Autoencoders and Weak Class Supervision](https://mlsanity.com/paper/1810.13104/) (2018)
- [REAL-M: Towards Speech Separation on Real Mixtures](https://mlsanity.com/paper/2110.10812/) (2021)
- [Continual self-training with bootstrapped remixing for speech enhancement](https://mlsanity.com/paper/2110.10103/) (2021)

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

- [Weakly-supervised Audio Separation via Bi-modal Semantic Similarity](https://mlsanity.com/paper/2404.01740/) (2024)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [OpenSep: Leveraging Large Language Models with Textual Inversion for Open World Audio Separation](https://mlsanity.com/paper/2409.19270/) (2024)
- [MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic Alignment](https://mlsanity.com/paper/2503.10287/) (2025)
- [Speech Separation with Pretrained Frontend to Minimize Domain Mismatch](https://mlsanity.com/paper/2411.03085/) (2024)
- [Speech Separation based on Contrastive Learning and Deep Modularization](https://mlsanity.com/paper/2305.10652/) (2023)
- [Text-Queried Audio Source Separation via Hierarchical Modeling](https://mlsanity.com/paper/2505.21025/) (2025)

## Abstract (arXiv)

We introduce two unsupervised source separation methods, which involve self-supervised training from single-channel two-source speech mixtures. Our first method, mixture permutation invariant training (MixPIT), enables learning a neural network model which separates the underlying sources via a challenging proxy task without supervision from the reference sources. Our second method, cyclic mixture permutation invariant training (MixCycle), uses MixPIT as a building block in a cyclic fashion for continuous learning. MixCycle gradually converts the problem from separating mixtures of mixtures into separating single mixtures. We compare our methods to common supervised and unsupervised baselines: permutation invariant training with dynamic mixing (PIT-DM) and mixture invariant training (MixIT). We show that MixCycle outperforms MixIT and reaches a performance level very close to the supervised baseline (PIT-DM) while circumventing the over-separation issue of MixIT. Also, we propose a self-evaluation technique inspired by MixCycle that estimates model performance without utilizing any reference sources. We show that it yields results consistent with an evaluation on reference sources (LibriMix) and also with an informal listening test conducted on a real-life mixtures dataset (REAL-M).