# Monaural source separation: From anechoic to reverberant environments

arXiv: https://arxiv.org/abs/2111.07578 · HTML: https://mlsanity.com/paper/2111.07578/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2021.

## Built on by (most cited first, 9 of 9)

- [Mixture Encoder for Joint Speech Separation and Recognition](https://mlsanity.com/paper/2306.12173/) (2023)
- [Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation](https://mlsanity.com/paper/2210.15305/) (2022)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments](https://mlsanity.com/paper/2310.06125/) (2023)
- [On Data Sampling Strategies for Training Neural Network Speech Separation Models](https://mlsanity.com/paper/2304.07142/) (2023)
- [Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription](https://mlsanity.com/paper/2309.08454/) (2023)
- [Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition](https://mlsanity.com/paper/2406.08914/) (2024)
- [SonicSieve: Bringing Directional Speech Extraction to Smartphones Using Acoustic Microstructures](https://mlsanity.com/paper/2504.10793/) (2025)
- [Error Analysis in a Modular Meeting Transcription System](https://mlsanity.com/paper/2509.10143/) (2025)

## Abstract (arXiv)

Impressive progress in neural network-based single-channel speech source separation has been made in recent years. But those improvements have been mostly reported on anechoic data, a situation that is hardly met in practice. Taking the SepFormer as a starting point, which achieves state-of-the-art performance on anechoic mixtures, we gradually modify it to optimize its performance on reverberant mixtures. Although this leads to a word error rate improvement by 7 percentage points compared to the standard SepFormer implementation, the system ends up with only marginally better performance than a PIT-BLSTM separation system, that is optimized with rather straightforward means. This is surprising and at the same time sobering, challenging the practical usefulness of many improvements reported in recent years for monaural source separation on nonreverberant data.