# SAMbA: Speech enhancement with Asynchronous ad-hoc Microphone Arrays

arXiv: https://arxiv.org/abs/2307.16582 · HTML: https://mlsanity.com/paper/2307.16582/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 6 of 6)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [FaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing](https://mlsanity.com/paper/1909.13387/) (2019)
- [DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays](https://mlsanity.com/paper/2011.01714/) (2020)
- [Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling](https://mlsanity.com/paper/1808.00096/) (2018)
- [DNN-Based Distributed Multichannel Mask Estimation for Speech Enhancement in Microphone Arrays](https://mlsanity.com/paper/2002.06016/) (2020)

## Built on by (most cited first, 1 of 1)

- [Distributed Asynchronous Device Speech Enhancement via Windowed Cross-Attention](https://mlsanity.com/paper/2507.16104/) (2025)

## Abstract (arXiv)

Speech enhancement in ad-hoc microphone arrays is often hindered by the asynchronization of the devices composing the microphone array. Asynchronization comes from sampling time offset and sampling rate offset which inevitably occur when the microphones are embedded in different hardware components. In this paper, we propose a deep neural network (DNN)-based speech enhancement solution that is suited for applications in ad-hoc microphone arrays because it is distributed and copes with asynchronization. We show that asynchronization has a limited impact on the spatial filtering and mostly affects the performance of the DNNs. Instead of resynchronising the signals, which requires costly processing steps, we use an attention mechanism which makes the DNNs, thus our whole pipeline, robust to asynchronization. We also show that the attention mechanism leads to the asynchronization parameters in an unsupervised manner.