# Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments

arXiv: https://arxiv.org/abs/2501.09394 · HTML: https://mlsanity.com/paper/2501.09394/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (2018)
- [Acoustic Scene Classification](https://mlsanity.com/paper/1411.3715/) (2014)
- [Quantum-Inspired Machine Learning: a Survey](https://mlsanity.com/paper/2308.11269/) (2023)
- [Acoustic Scene Classification: A Competition Review](https://mlsanity.com/paper/1808.02357/) (2018)

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

- [Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications](https://mlsanity.com/paper/2601.02432/) (2026)
- [StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting](https://mlsanity.com/paper/2605.26523/) (2026)

## Abstract (arXiv)

The proliferation of Internet of Things (IoT) devices equipped with acoustic sensors necessitates robust acoustic scene classification (ASC) capabilities, even in noisy and data-limited environments. Traditional machine learning methods often struggle to generalize effectively under such conditions. To address this, we introduce Q-ASC, a novel Quantum-Inspired Acoustic Scene Classifier that leverages the power of quantum-inspired transformers. By integrating quantum concepts like superposition and entanglement, Q-ASC achieves superior feature learning and enhanced noise resilience compared to classical models. Furthermore, we introduce a Quantum Variational Autoencoder (QVAE) based data augmentation technique to mitigate the challenge of limited labeled data in IoT deployments. Extensive evaluations on the Tampere University of Technology (TUT) Acoustic Scenes 2016 benchmark dataset demonstrate that Q-ASC achieves remarkable accuracy between 68.3% and 88.5% under challenging conditions, outperforming state-of-the-art methods by over 5% in the best case. This research paves the way for deploying intelligent acoustic sensing in IoT networks, with potential applications in smart homes, industrial monitoring, and environmental surveillance, even in adverse acoustic environments.