Abstract
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.
Abstract (translated)
物联网（IoT）设备中配备声学传感器的数量激增，要求在嘈杂且数据有限的环境中具备强大的声景分类（ASC）能力。传统机器学习方法在这种条件下往往难以有效泛化。为此，我们引入了Q-ASC，这是一种基于量子启发式变压器的新型量子启发式声景分类器，利用了诸如叠加和纠缠等量子概念的力量，从而在特征学习和抗噪性能方面超越经典模型。此外，为了缓解物联网部署中标签数据不足的问题，我们还提出了一种基于量子变分自动编码器（QVAE）的数据增强技术。 我们在坦佩雷理工大学（TUT）2016年声景基准数据集上进行了广泛的评估，结果表明，在挑战性的条件下，Q-ASC的准确性达到了惊人的68.3%到88.5%，在最佳情况下比现有方法高出超过5%。这项研究为在物联网网络中部署智能声学传感铺平了道路，并且潜在的应用包括智能家居、工业监控和环境监视，即使是在不良的声学环境中也是如此。
URL
https://arxiv.org/abs/2501.09394
