# Multi-channel target speech extraction with channel decorrelation and target speaker adaptation

arXiv: https://arxiv.org/abs/2010.09191 · HTML: https://mlsanity.com/paper/2010.09191/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Look Once to Hear: Target Speech Hearing with Noisy Examples](https://mlsanity.com/paper/2405.06289/) (2024)
- [End-to-End Multi-Microphone Speaker Extraction Using Relative Transfer Functions](https://mlsanity.com/paper/2502.06285/) (2025)

## Abstract (arXiv)

The end-to-end approaches for single-channel target speech extraction have attracted widespread attention. However, the studies for end-to-end multi-channel target speech extraction are still relatively limited. In this work, we propose two methods for exploiting the multi-channel spatial information to extract the target speech. The first one is using a target speech adaptation layer in a parallel encoder architecture. The second one is designing a channel decorrelation mechanism to extract the inter-channel differential information to enhance the multi-channel encoder representation. We compare the proposed methods with two strong state-of-the-art baselines. Experimental results on the multi-channel reverberant WSJ0 2-mix dataset demonstrate that our proposed methods achieve up to 11.2% and 11.5% relative improvements in SDR and SiSDR respectively, which are the best reported results on this task to the best of our knowledge.