# Flexible Multichannel Speech Enhancement for Noise-Robust Frontend

arXiv: https://arxiv.org/abs/2406.04552 · HTML: https://mlsanity.com/paper/2406.04552/

## Key facts

- Cited by 3 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [ICASSP 2022 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2202.13288/) (2022)
- [End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation](https://mlsanity.com/paper/1910.14104/) (2019)
- [How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR](https://mlsanity.com/paper/2201.06685/) (2022)
- [End-to-End Multi-speaker Speech Recognition with Transformer](https://mlsanity.com/paper/2002.03921/) (2020)
- [Neural Target Speech Extraction: An Overview](https://mlsanity.com/paper/2301.13341/) (2023)
- [Far-Field Automatic Speech Recognition](https://mlsanity.com/paper/2009.09395/) (2020)
- [Recognizing Overlapped Speech in Meetings: A Multichannel Separation Approach Using Neural Networks](https://mlsanity.com/paper/1810.03655/) (2018)
- [DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement](https://mlsanity.com/paper/2106.15813/) (2021)
- [VarArray: Array-Geometry-Agnostic Continuous Speech Separation](https://mlsanity.com/paper/2110.05745/) (2021)
- [Neural Speech Separation Using Spatially Distributed Microphones](https://mlsanity.com/paper/2004.13670/) (2020)
- [Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation](https://mlsanity.com/paper/2011.15003/) (2020)
- [One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement](https://mlsanity.com/paper/2110.10330/) (2021)
- [Mask-based Neural Beamforming for Moving Speakers with Self-Attention-based Tracking](https://mlsanity.com/paper/2205.03568/) (2022)
- [Scene-Agnostic Multi-Microphone Speech Dereverberation](https://mlsanity.com/paper/2010.11875/) (2020)

## Built on by (most cited first, 3 of 3)

- [AmbiDrop: Array-Agnostic Speech Enhancement Using Ambisonics Encoding and Dropout-Based Learning](https://mlsanity.com/paper/2509.14855/) (2025)
- [Robust Speech Recognition with Schr"odinger Bridge-Based Speech Enhancement](https://mlsanity.com/paper/2505.04237/) (2025)
- [AmbiDrop: Ambisonics-Based Array-Agnostic Neural Speech Enhancement](https://mlsanity.com/paper/2607.00548/) (2026)

## Abstract (arXiv)

This paper proposes a flexible multichannel speech enhancement system with the main goal of improving robustness of automatic speech recognition (ASR) in noisy conditions. The proposed system combines a flexible neural mask estimator applicable to different channel counts and configurations and a multichannel filter with automatic reference selection. A transform-attend-concatenate layer is proposed to handle cross-channel information in the mask estimator, which is shown to be effective for arbitrary microphone configurations. The presented evaluation demonstrates the effectiveness of the flexible system for several seen and unseen compact array geometries, matching the performance of fixed configuration-specific systems. Furthermore, a significantly improved ASR performance is observed for configurations with randomly-placed microphones.