In this paper, we provide a thorough review of novel machine learning (ML) models for anomalous sound detection (ASD). We focus on deploying models to highly constrained, embedded systems and tiny ML, and using single-channel sound as the data input. The survey includes only the works published in 2020 and later. Researchers address the anomaly detection task in various ways, borrowing models and techniques from such fields as speech processing, audio generation, and even computer vision. However, it is not clear which of these are suitable for embedded systems, meeting their constraints such as memory or compute. To address that, we provide a deep analysis of these models and optimization techniques applied to meet the design criteria for embedded platforms. We consider both deep learning and classical ML methods. We define categories for the anomaly detection methods depending on the approach taken to provide a structure and simplify the comparison of methods. We aim to provide a guideline on how to develop ASD systems and how to efficiently deploy the models on the embedded platforms.
Grzymkowski et al. (Fri,) studied this question.