engleski

Detecting Anomalies from Sound in the Context of Smart Home Using Unsupervised Learning on Edge Devices

This thesis researches unsupervised sound anomaly detection, as there are many use-cases, including factories and smart homes, with transformative potential. To make sure that anomalies are identified efficiently and with low-latency without dependence on an Internet connection, which is needed for multiple uses, we use an edge device to infer closer to the sensor location. Since we work with time-series data, our architecture is made of LSTM units. We train the network, vary multiple neural network sizes on multiple devices and evaluate the results with metrics like inference time, accuracy, F1 and ROC. The results point out that the best performing network has a generalization power in around one-tenth of a second and can be performed on an affordable edge device. We propose further research steps to improve the neural network architecture and start applying the solution in practice.

unsupervised learning ; sound anomaly detection ; autoencoder ; LSTM ; edge computing ; IoT

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