5 papers · 1 filter
Cloud-Boosted Low-Compute Multi-Channel Speech Enhancement
Xulin Fan, Juan Azcarreta, Ashutosh Pandey +5
Low-latency, low-compute speech enhancement is essential for wearable devices with real-time communication requirements, but strict computational constraints significantly limit on…
Controlling the Parameterized Multi-channel Wiener Filter using a tiny neural network
Eric Grinstein, Ashutosh Pandey, Cole Li +6
Noise suppression and speech distortion are two important aspects to be balanced when designing multi-channel Speech Enhancement (SE) algorithms. Although neural network models hav…
Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training and Unimodal Deployment
Joanna Hong, Sanjeel Parekh, Honglie Chen +4
Building reliable speech systems often requires combining multiple modalities, like audio and visual cues. While such multimodal solutions frequently lead to improvements in perfor…
On the Importance of Neural Wiener Filter for Resource Efficient Multichannel Speech Enhancement
Tsun-An Hsieh, Jacob Donley, Daniel Wong +2
We introduce a time-domain framework for efficient multichannel speech enhancement, emphasizing low latency and computational efficiency. This framework incorporates two compact de…
Multi-Channel Speech Enhancement using Graph Neural Networks
Panagiotis Tzirakis, Anurag Kumar, Jacob Donley
Multi-channel speech enhancement aims to extract clean speech from a noisy mixture using signals captured from multiple microphones. Recently proposed methods tackle this problem b…