# AmbiDrop: Ambisonics-Based Array-Agnostic Neural Speech Enhancement

arXiv: https://arxiv.org/abs/2607.00548 · HTML: https://mlsanity.com/paper/2607.00548/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## 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, 13 of 13)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Project Aria: A New Tool for Egocentric Multi-Modal AI Research](https://mlsanity.com/paper/2308.13561/) (2023)
- [End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation](https://mlsanity.com/paper/1910.14104/) (2019)
- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [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)
- [One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement](https://mlsanity.com/paper/2110.10330/) (2021)
- [Microphone Array Signal Processing and Deep Learning for Speech Enhancement](https://mlsanity.com/paper/2501.07215/) (2025)
- [Microphone Array Generalization for Multichannel Narrowband Deep Speech Enhancement](https://mlsanity.com/paper/2107.12601/) (2021)
- [Advances in Microphone Array Processing and Multichannel Speech Enhancement](https://mlsanity.com/paper/2502.09037/) (2025)
- [Ambisonics Encoding For Arbitrary Microphone Arrays Incorporating Residual Channels For Binaural Reproduction](https://mlsanity.com/paper/2402.17362/) (2024)
- [Flexible Multichannel Speech Enhancement for Noise-Robust Frontend](https://mlsanity.com/paper/2406.04552/) (2024)
- [AmbiDrop: Array-Agnostic Speech Enhancement Using Ambisonics Encoding and Dropout-Based Learning](https://mlsanity.com/paper/2509.14855/) (2025)

## Abstract (arXiv)

Multichannel Deep Neural Networks (DNNs) have significantly improved speech enhancement performance; however, they typically remain constrained by reliance on fixed microphone array geometries, leading to poor generalization on unseen or irregular configurations. Current array-agnostic approaches often rely on high-complexity architectures or massive, diverse datasets, yet they still struggle to generalize to out-of-distribution layouts. In this paper, we present an in-depth analysis of AmbiDrop, a recently proposed framework that achieves geometry independence by leveraging ideal Ambisonics as the DNN input. By employing a channel-wise dropout layer during training to simulate Ambisonics encoding errors, AmbiDrop decouples the learning process from the physical sensor arrangement. During inference, microphone signals from arbitrary array configurations are transformed into the Ambisonics domain via Ambisonics Signal Matching (ASM) before processing. Extensive experiments demonstrate that AmbiDrop maintains high robustness across a diverse suite of unseen simulated arrays and real-world recordings. Furthermore, our results show that the framework is resilient to sensor failures and remains effective even with reduced network scales, making it highly suitable for deployment on resource-constrained edge devices and versatile wearable hardware.