Multi-Scale Temporal Frequency Convolutional Network With Axial Attention for Speech Enhancement
By Guochang Zhang, Libiao Yu, Chunliang Wang, Jianqiang Wei. 2022
78 citations recorded by the source corpus.
Speech quality is often degraded by acoustic echoes, background noise, and reverberation. In this paper, we propose a system consisting of deep learning and signal processing to simultaneously suppress echoes, noise, and reverberation. For the deep learning, we design a novel speech dense-prediction backbone. For the signal processing, a linear acoustic echo canceller is used as conditional information for deep learning. To improve the performance of the speech dense-prediction backbone, strategies such as a microphone and reference phase encoder, multi-scale time-frequency processing, and streaming axial attention are designed. The proposed system ranked first in both AEC and DNS Challenge (non-personal track) of ICASSP 2022. In addition, this backbone has also been extended to the multi-channel speech enhancement task, and placed second in ICASSP 2022 L3DAS22 Challenge1.
Subfields: Artificial Intelligence, Computational Mechanics, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ross Cutler
- Vishak Gopal
- Sebastian Braun
- Chandan K. A. Reddy
- Robert Aichner
- Harishchandra Dubey
- Hannes Gamper
- Sriram Srinivasan
In Computational Mechanics
- Vishak Gopal
- Ross Cutler
- Chandan K. A. Reddy
- Robert Aichner
- Hannes Gamper
- Harishchandra Dubey
- Sebastian Braun
- Sriram Srinivasan
In Signal Processing
- Ross Cutler
- Vishak Gopal
- Chandan K. A. Reddy
- Robert Aichner
- Hannes Gamper
- Sebastian Braun
- Harishchandra Dubey
- Sriram Srinivasan
Citations in the corpus
- Icassp 2022 Deep Noise Suppression Challenge (2022, background): “The winning team for Track 1 [25] also won the ICASSP 2022 AEC Challenge [26], and demonstrates a single model can provide excellent AEC, DNS, and WAcc performance.”
- ICASSP 2023 Deep Noise Suppression Challenge (2023, background): “(vi) Track-1 and Track-2 has their separate subjective evaluations to ensure we have headset testclips evaluated together, and speakerphone testclips evaluated together.”
- ICASSP 2022 Acoustic Echo Cancellation Challenge (2022, methodology): “The 1st place model [26] also won the ICASSP 2022 DNS Challenge [27], providing the only model that didn’t induce SIG [28] distortion in that challenge.”