# AmbiDrop: Array-Agnostic Speech Enhancement Using Ambisonics Encoding and Dropout-Based Learning

arXiv: https://arxiv.org/abs/2509.14855 · HTML: https://mlsanity.com/paper/2509.14855/

## Key facts

- Cited by 2 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 11 of its 21 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## 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, 11 of 11)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation](https://mlsanity.com/paper/1910.14104/) (2019)
- [EasyCom: An Augmented Reality Dataset to Support Algorithms for Easy Communication in Noisy Environments](https://mlsanity.com/paper/2107.04174/) (2021)
- [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)
- [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)
- [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)
- [Hierarchical Modeling of Spatial Cues via Spherical Harmonics for Multi-Channel Speech Enhancement](https://mlsanity.com/paper/2309.10393/) (2023)

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

- [AmbiDrop: Ambisonics-Based Array-Agnostic Neural Speech Enhancement](https://mlsanity.com/paper/2607.00548/) (2026)
- [Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution](https://mlsanity.com/paper/2607.18658/) (2026)

## Abstract (arXiv)

Multichannel speech enhancement leverages spatial cues to improve intelligibility and quality, but most learning-based methods rely on specific microphone array geometry, unable to account for geometry changes. To mitigate this limitation, current array-agnostic approaches employ large multi-geometry datasets but may still fail to generalize to unseen layouts. We propose AmbiDrop (Ambisonics with Dropouts), an Ambisonics-based framework that encodes arbitrary array recordings into the spherical harmonics domain using Ambisonics Signal Matching (ASM). A deep neural network is trained on simulated Ambisonics data, combined with channel dropout for robustness against array-dependent encoding errors, therefore omitting the need for a diverse microphone array database. Experiments show that while the baseline and proposed models perform similarly on the training arrays, the baseline degrades on unseen arrays. In contrast, AmbiDrop consistently improves SI-SDR, PESQ, and STOI, demonstrating strong generalization and practical potential for array-agnostic speech enhancement.