用量子启发模型提升物联网嘈杂环境下的声音场景识别准确率
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments
- 引入量子启发注意力机制，增强特征学习与抗噪能力
- 在TUT 2016数据集上达到68.3%~88.5%准确率，优于现有方法超5%
- 适合智能家庭、工业监测等低数据量、高噪声场景应用
大量配备声学传感器的物联网（IoT）设备亟需在噪声大、数据少的环境下具备鲁棒的声学场景分类（ASC）能力。传统机器学习方法在此类条件下泛化性能差。为此，本文提出一种量子启发声学场景分类器Q-ASC，融合量子叠加与纠缠概念，显著提升特征学习能力与抗噪性。同时，设计基于量子变分自编码器（QVAE）的数据增强方法，缓解物联网部署中标签数据不足的问题。在坦佩雷理工大学TUT声学场景2016基准数据集上的实验表明，Q-ASC在复杂条件下实现68.3%至88.5%的准确率，最佳情况下比当前最优方法高出超过5%。本研究为物联网网络中智能声学感知的部署提供了可行路径，适用于智能家居、工业监控和环境监测等恶劣声学环境。
原文摘要 · Abstract (English)
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.
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