用轻量注意力与分离卷积提升无监督音频异常检测效率
Low-complexity Attention-based Unsupervised Anomalous Sound Detection exploiting Separable Convolutions and Angular Loss
- 引入注意力机制与分离卷积，高效捕捉音频时频特征
- 在DCASE 2020任务2数据集上准确率优于现有方法，参数更少
- 适合资源受限场景的实时音频异常检测应用
本文提出一种新型深度神经网络，旨在提升无监督声音异常检测的效率与效果。该模型利用注意力模块和分离卷积，从音频数据中识别关键时频模式，以区分正常与异常声音，同时显著降低计算复杂度。通过DCASE 2020挑战赛任务2数据集的大量实验验证，该方法在保持更高异常检测准确率的同时，参数数量少于当前最优方法。实现细节、代码及预训练模型可在https://github.com/michaelneri/unsupervised-audio-anomaly-detection获取。
原文摘要 · Abstract (English)
In this work, a novel deep neural network, designed to enhance the efficiency and effectiveness of unsupervised sound anomaly detection, is presented. The proposed model exploits an attention module and separable convolutions to identify salient time-frequency patterns in audio data to discriminate between normal and anomalous sounds with reduced computational complexity. The approach is validated through extensive experiments using the Task 2 dataset of the DCASE 2020 challenge. Results demonstrate superior performance in terms of anomaly detection accuracy while having fewer parameters than state-of-the-art methods. Implementation details, code, and pre-trained models are available in https://github.com/michaelneri/unsupervised-audio-anomaly-detection.
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