# Building Corpora for Single-Channel Speech Separation Across Multiple Domains

arXiv: https://arxiv.org/abs/1811.02641 · HTML: https://mlsanity.com/paper/1811.02641/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Learning to Separate Voices by Spatial Regions](https://mlsanity.com/paper/2207.04203/) (2022)
- [Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation](https://mlsanity.com/paper/2101.00701/) (2021)
- [Unleashing the Power of Natural Audio Featuring Multiple Sound Sources](https://mlsanity.com/paper/2504.17782/) (2025)

## Abstract (arXiv)

To date, the bulk of research on single-channel speech separation has been conducted using clean, near-field, read speech, which is not representative of many modern applications. In this work, we develop a procedure for constructing high-quality synthetic overlap datasets, necessary for most deep learning-based separation frameworks. We produced datasets that are more representative of realistic applications using the CHiME-5 and Mixer 6 corpora and evaluate standard methods on this data to demonstrate the shortcomings of current source-separation performance. We also demonstrate the value of a wide variety of data in training robust models that generalize well to multiple conditions.