Deep Ad-hoc Beamforming
2018 · Xiao-Lei Zhang
Abstract
Far-field speech processing is an important and challenging problem. In this paper, we propose \textit\{deep 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 \textit\{ad-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
Authors
(none)
Tags
Stats
Related papers
- Deep Ad-hoc Beamforming Based On Speaker Extraction For Target-dependent Speech Separation (2020)7.50
- Deep Long Short-term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition (2017)13.23
- A Unified Multichannel Far-field Speech Recognition System: Combining Neural Beamforming With Attention Based End-to-end Model (2024)0.00
- Embedding And Beamforming: All-neural Causal Beamformer For Multichannel Speech Enhancement (2021)13.05
- Fasnet: Low-latency Adaptive Beamforming For Multi-microphone Audio Processing (2019)0.00
- Deep Learning Based Stage-wise Two-dimensional Speaker Localization With Large Ad-hoc Microphone Arrays (2022)3.58
- Multichannel Speech Enhancement Without Beamforming (2021)9.41
- Dnn-free Low-latency Adaptive Speech Enhancement Based On Frame-online Beamforming Powered By Block-online Fastmnmf (2022)0.00