# Unsupervised training of a deep clustering model for multichannel blind source separation

arXiv: https://arxiv.org/abs/1904.01340 · HTML: https://mlsanity.com/paper/1904.01340/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [AudioScopeV2: Audio-Visual Attention Architectures for Calibrated Open-Domain On-Screen Sound Separation](https://mlsanity.com/paper/2207.10141/) (2022)
- [Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation](https://mlsanity.com/paper/2105.03544/) (2021)
- [Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation](https://mlsanity.com/paper/2106.07843/) (2021)
- [Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data](https://mlsanity.com/paper/2105.04727/) (2021)
- [MixCycle: Unsupervised Speech Separation via Cyclic Mixture Permutation Invariant Training](https://mlsanity.com/paper/2202.03875/) (2022)
- [Improving On-Screen Sound Separation for Open-Domain Videos with Audio-Visual Self-Attention](https://mlsanity.com/paper/2106.09669/) (2021)
- [Unsupervised Music Source Separation Using Differentiable Parametric Source Models](https://mlsanity.com/paper/2201.09592/) (2022)
- [Learning to Separate Voices by Spatial Regions](https://mlsanity.com/paper/2207.04203/) (2022)
- [UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures](https://mlsanity.com/paper/2305.20054/) (2023)
- [Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data](https://mlsanity.com/paper/2301.11981/) (2023)
- [Multi-scale clustering and source separation of InSight mission seismic data](https://mlsanity.com/paper/2305.16189/) (2023)
- [Reverberation as Supervision for Speech Separation](https://mlsanity.com/paper/2211.08303/) (2022)
- [End-to-End Multi-speaker ASR with Independent Vector Analysis](https://mlsanity.com/paper/2204.00218/) (2022)
- [Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational Environments](https://mlsanity.com/paper/2207.07296/) (2022)

## Abstract (arXiv)

We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on deep clustering requires strong supervision and elaborate on why this is a limitation. We demonstrate that (a) the single-channel deep clustering system trained according to the proposed scheme alone is able to achieve a similar performance as the multi-channel teacher in terms of word error rates and (b) initializing the spatial clustering approach with the deep clustering result yields a relative word error rate reduction of 26 % over the unsupervised teacher.