# Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information

arXiv: https://arxiv.org/abs/1811.01531 · HTML: https://mlsanity.com/paper/1811.01531/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [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)
- [ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior](https://mlsanity.com/paper/2505.05657/) (2025)
- [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)
- [Multi-Microphone Speaker Separation by Spatial Regions](https://mlsanity.com/paper/2303.07143/) (2023)
- [UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures](https://mlsanity.com/paper/2305.20054/) (2023)
- [Reverberation as Supervision for Speech Separation](https://mlsanity.com/paper/2211.08303/) (2022)
- [Pre-trained Spatial Priors on Multichannel NMF for Music Source Separation](https://mlsanity.com/paper/2310.05821/) (2023)

## Abstract (arXiv)

We present a monophonic source separation system that is trained by only observing mixtures with no ground truth separation information. We use a deep clustering approach which trains on multi-channel mixtures and learns to project spectrogram bins to source clusters that correlate with various spatial features. We show that using such a training process we can obtain separation performance that is as good as making use of ground truth separation information. Once trained, this system is capable of performing sound separation on monophonic inputs, despite having learned how to do so using multi-channel recordings.