# Multi-Channel Replay Speech Detection using Acoustic Maps

arXiv: https://arxiv.org/abs/2602.16399 · HTML: https://mlsanity.com/paper/2602.16399/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild](https://mlsanity.com/paper/2210.02437/) (2022)
- [ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems](https://mlsanity.com/paper/1904.03365/) (2019)
- [Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method](https://mlsanity.com/paper/2003.08225/) (2020)
- [Low-complexity Attention-based Unsupervised Anomalous Sound Detection exploiting Separable Convolutions and Angular Loss](https://mlsanity.com/paper/2410.08919/) (2024)
- [Multi-channel Replay Speech Detection using an Adaptive Learnable Beamformer](https://mlsanity.com/paper/2502.13473/) (2025)
- [Impact of Microphone Array Mismatches to Learning-based Replay Speech Detection](https://mlsanity.com/paper/2503.07357/) (2025)
- [Acoustic Simulation Framework for Multi-channel Replay Speech Detection](https://mlsanity.com/paper/2509.14789/) (2025)

## Abstract (arXiv)

Replay attacks remain a critical vulnerability for automatic speaker verification systems, particularly in real-time voice assistant applications. In this work, we propose acoustic maps as a novel spatial feature representation for replay speech detection from multi-channel recordings. Derived from classical beamforming over discrete azimuth and elevation grids, acoustic maps encode directional energy distributions that reflect physical differences between human speech radiation and loudspeaker-based replay. A lightweight convolutional neural network is designed to operate on this representation, achieving competitive performance on the ReMASC dataset with approximately 6k trainable parameters. Experimental results show that acoustic maps provide a compact and physically interpretable feature space for replay attack detection across different devices and acoustic environments.