Channel-Attention Dense U-Net for Multichannel Speech Enhancement

01/30/2020
by   Bahareh Tolooshams, et al.
9

Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary mask that is applied to the mixture in the time-frequency domain to produce the clean speech. Despite the great performance in the single-channel setting, these frameworks lag in performance in the multichannel setting as the majority of these methods a) fail to exploit the available spatial information fully, and b) still treat the deep architecture as a black box which may not be well-suited for multichannel audio processing. This paper addresses these drawbacks, a) by utilizing complex ratio masking instead of masking on the magnitude of the spectrogram, and more importantly, b) by introducing a channel-attention mechanism inside the deep architecture to mimic beamforming. We propose Channel-Attention Dense U-Net, in which we apply the channel-attention unit recursively on feature maps at every layer of the network, enabling the network to perform non-linear beamforming. We demonstrate the superior performance of the network against the state-of-the-art approaches on the CHiME-3 dataset.

READ FULL TEXT
10/27/2021

Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions

The deep learning based time-domain models, e.g. Conv-TasNet, have shown...
08/27/2021

Full Attention Bidirectional Deep Learning Structure for Single Channel Speech Enhancement

As the cornerstone of other important technologies, such as speech recog...
03/23/2022

FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement

Previously proposed FullSubNet has achieved outstanding performance in D...
02/03/2021

Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses

Deep complex U-Net structure and convolutional recurrent network (CRN) s...
01/15/2021

AMFFCN: Attentional Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement

Audio-visual speech enhancement system is regarded to be one of promisin...
07/28/2021

CycleGAN-based Non-parallel Speech Enhancement with an Adaptive Attention-in-attention Mechanism

Non-parallel training is a difficult but essential task for DNN-based sp...
02/15/2018

Deep Learning Based Speech Beamforming

Multi-channel speech enhancement with ad-hoc sensors has been a challeng...