# DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays

arXiv: https://arxiv.org/abs/2011.01714 · HTML: https://mlsanity.com/paper/2011.01714/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

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

- [UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement](https://mlsanity.com/paper/2510.20441/) (2025)
- [Learning to Rank Microphones for Distant Speech Recognition](https://mlsanity.com/paper/2104.02819/) (2021)
- [Linguistic Knowledge Transfer Learning for Speech Enhancement](https://mlsanity.com/paper/2503.07078/) (2025)
- [Frequency-Weighted Training Losses for Phoneme-Level DNN-based Speech Enhancement](https://mlsanity.com/paper/2506.18714/) (2025)
- [Challenges and Opportunities in Multi-device Speech Processing](https://mlsanity.com/paper/2206.15432/) (2022)
- [CaSNet: Compress-and-Send Network Based Multi-Device Speech Enhancement Model for Distributed Microphone Arrays](https://mlsanity.com/paper/2601.17711/) (2026)
- [Time-Frequency Weighted Losses for Phoneme Reconstruction in DNN-Based Speech Enhancement](https://mlsanity.com/paper/2606.21635/) (2026)

## Abstract (arXiv)

Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In this paper, we propose to extend a previously introduced distributed DNN-based time-frequency mask estimation scheme that can efficiently use spatial information in form of so-called compressed signals which are pre-filtered target estimations. We study the performance of this algorithm under realistic acoustic conditions and investigate practical aspects of its optimal application. We show that the nodes in the microphone array cooperate by taking profit of their spatial coverage in the room. We also propose to use the compressed signals not only to convey the target estimation but also the noise estimation in order to exploit the acoustic diversity recorded throughout the microphone array.