# Inplace Gated Convolutional Recurrent Neural Network For Dual-channel Speech Enhancement

arXiv: https://arxiv.org/abs/2107.11968 · HTML: https://mlsanity.com/paper/2107.11968/

## Key facts

- Cited by 5 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 26 Jul 2021.

## Built on by (most cited first, 5 of 5)

- [SICRN: Advancing Speech Enhancement through State Space Model and Inplace Convolution Techniques](https://mlsanity.com/paper/2402.14225/) (2024)
- [The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition](https://mlsanity.com/paper/2202.10017/) (2022)
- [ARiSE: Auto-Regressive Multi-Channel Speech Enhancement](https://mlsanity.com/paper/2505.22051/) (2025)
- [Efficient Multi-Channel Speech Enhancement with Spherical Harmonics Injection for Directional Encoding](https://mlsanity.com/paper/2309.10832/) (2023)
- [Speech Enhancement with Intelligent Neural Homomorphic Synthesis](https://mlsanity.com/paper/2210.15853/) (2022)

## Abstract (arXiv)

For dual-channel speech enhancement, it is a promising idea to design an end-to-end model based on the traditional array signal processing guideline and the manifold space of multi-channel signals. We found that the idea above can be effectively implemented by the classical convolutional recurrent neural networks (CRN) architecture. We propose a very compact in place gated convolutional recurrent neural network (inplace GCRN) for end-to-end multi-channel speech enhancement, which utilizes inplace-convolution for frequency pattern extraction and reconstruction. The inplace characteristics efficiently preserve spatial cues in each frequency bin for channel-wise long short-term memory neural networks (LSTM) tracing the spatial source. In addition, we come up with a new spectrum recovery method by predict amplitude mask, mapping, and phase, which effectively improves the speech quality.