- 期刊
- OpenAccess
Acoustic Anomaly Detection Using Multilayer Neural Networks and Semantic Pointers
摘要
In this paper we provide an auditory processing system with higher biological plausibility than previous studies to solve the problem of acoustic anomaly detection in household environment. First, in the proposed system the log filter bank is adopted for extracting audio features, simulating the function of the peripheral auditory system (outer, middle and inner ears to auditory nerves). Next, we use multi-layer neural networks to imitate auditory cortex in human brains, in order to extract abstract semantic contents. Then, a semantic pointer architecture model is used to imitate prefrontal cortex, basal ganglia, and thalamus, in which the anomaly is detected using symbol-like rules. Compared with other anomaly detection methods with different biological plausibility in performance, our proposed method gets the best result on the testing set, with 0.956 AUC. Meanwhile, it takes less computational time to detect the anomaly. Hence, it is suitable for detecting acoustic anomalies in real-world cases.
延伸閱讀
- Wang, S. H. (2015). Activity Recognition with Anomaly Detection Using Non-Parametric Topic Modeling [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2015.11075
- Sun, L., He, J., Yin, X., Zhang, Y., Chen, J. H., Kron, T., & Su, M. Y. (2018). Deep Learning based Anomaly Detection. 電腦學刊, 29(6), 148-157. https://doi.org/10.3966/199115992018122906014
- Balara, D., Timko, J., Zilková, J., & Leso, M. (2017). NEURAL NETWORKS APPLICATION FOR MECHANICAL PARAMETERS IDENTIFICATION OF ASYNCHRONOUS MOTOR. Neural Network World, 27(3), 259-270. https://doi.org/10.14311/NNW.2017.27.013
- 薛吉全（2019）。Adaptive Anomaly Detection via 3D Convolutional Neural Network〔碩士論文，淡江大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0002-1609201911570400
- Che, C. L. (2018). A Hybrid Supervised Learning Approach for Anomaly Detection in Social Networks [master's thesis, National Chiao Tung University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0030-0205201911063305