# Microphone Array Signal Processing and Deep Learning for Speech Enhancement

arXiv: https://arxiv.org/abs/2501.07215 · HTML: https://mlsanity.com/paper/2501.07215/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2025.

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

- [Improving multichannel speech enhancement through accurate room-acoustic simulations](https://mlsanity.com/paper/2606.31552/) (2026)
- [SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios](https://mlsanity.com/paper/2607.02343/) (2026)
- [Comparison of Knowledge Distillation Methods for Low-complexity Multi-microphone Speech Enhancement using the FT-JNF Architecture](https://mlsanity.com/paper/2507.19208/) (2025)
- [On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation](https://mlsanity.com/paper/2508.18833/) (2025)
- [Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics](https://mlsanity.com/paper/2607.04471/) (2026)

## Abstract (arXiv)

Multi-channel acoustic signal processing is a well-established and powerful tool to exploit the spatial diversity between a target signal and non-target or noise sources for signal enhancement. However, the textbook solutions for optimal data-dependent spatial filtering rest on the knowledge of second-order statistical moments of the signals, which have traditionally been difficult to acquire. In this contribution, we compare model-based, purely data-driven, and hybrid approaches to parameter estimation and filtering, where the latter tries to combine the benefits of model-based signal processing and data-driven deep learning to overcome their individual deficiencies. We illustrate the underlying design principles with examples from noise reduction, source separation, and dereverberation.