# Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge

arXiv: https://arxiv.org/abs/2202.12298 · HTML: https://mlsanity.com/paper/2202.12298/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2022.

## Built on by (most cited first, 4 of 4)

- [ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding](https://mlsanity.com/paper/2207.09514/) (2022)
- [Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation](https://mlsanity.com/paper/2307.12231/) (2023)
- [LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker Verification Models](https://mlsanity.com/paper/2311.16604/) (2023)
- [Joint Beamforming and Speaker-Attributed ASR for Real Distant-Microphone Meeting Transcription](https://mlsanity.com/paper/2410.21849/) (2024)

## Abstract (arXiv)

This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of speech enhancement with 3D Ambisonic microphones. The core of our approach combines Deep Neural Network (DNN) driven complex spectral mapping with linear beamformers such as the multi-frame multi-channel Wiener filter. Our proposed system has two DNNs and a linear beamformer in between. Both DNNs are trained to perform complex spectral mapping, using a combination of waveform and magnitude spectrum losses. The estimated signal from the first DNN is used to drive a linear beamformer, and the beamforming result, together with this enhanced signal, are used as extra inputs for the second DNN which refines the estimation. Then, from this new estimated signal, the linear beamformer and second DNN are run iteratively. The proposed method was ranked first in the challenge, achieving, on the evaluation set, a ranking metric of 0.984, versus 0.833 of the challenge baseline.