# Deep Ad-hoc Beamforming

arXiv: https://arxiv.org/abs/1811.01233 · HTML: https://mlsanity.com/paper/1811.01233/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Learning to Rank Microphones for Distant Speech Recognition](https://mlsanity.com/paper/2104.02819/) (2021)
- [Scaling sparsemax based channel selection for speech recognition with ad-hoc microphone arrays](https://mlsanity.com/paper/2103.15305/) (2021)

## Abstract (arXiv)

Far-field speech processing is an important and challenging problem. In this paper, we propose textitdeep ad-hoc beamforming, a deep-learning-based multichannel speech enhancement framework based on ad-hoc microphone arrays, to address the problem. It contains three novel components. First, it combines textitad-hoc microphone arrays with deep-learning-based multichannel speech enhancement, which reduces the probability of the occurrence of far-field acoustic environments significantly. Second, it groups the microphones around the speech source to a local microphone array by a supervised channel selection framework based on deep neural networks. Third, it develops a simple time synchronization framework to synchronize the channels that have different time delay. Besides the above novelties and advantages, the proposed model is also trained in a single-channel fashion, so that it can easily employ new development of speech processing techniques. Its test stage is also flexible in incorporating any number of microphones without retraining or modifying the framework. We have developed many implementations of the proposed framework and conducted an extensive experiment in scenarios where the locations of the speech sources are far-field, random, and blind to the microphones. Results on speech enhancement tasks show that our method outperforms its counterpart that works with linear microphone arrays by a considerable margin in both diffuse noise reverberant environments and point source noise reverberant environments. We have also tested the framework with different handcrafted features. Results show that although designing good features lead to high performance, they do not affect the conclusion on the effectiveness of the proposed framework.