Anomaly Localization in Audio via Feature Pyramid Matching.

Sound anomaly detection is a task that aims at identifying unusual or abnormal sounds within audio data. These sounds could be caused by different factors, such as background noise, equipment malfunctions, or unexpected events. Anomaly detection in sound is a well-studied topic, with a lot of research being done in the field. Anomaly localization refers to the process of identifying the specific location or region within a sample where an anomaly (or outlier) occurs. When applied to audio signals, anomaly localization can involve analyzing the spectral content of the sound to detect regions that deviate from the typical or expected pattern. In this study, we present a simple yet effective model based on the Student-Teacher Feature Pyramid Matching Method for locating anomalies in audio data. Utilizing the MIMII dataset by augmenting it with synthetic anomalies, we evaluate the method's accuracy. Our results demonstrate that the proposed model can accurately locate artificially created anomalies within the spectrograms, both in terms of time and frequency. This approach offers a promising solution for identifying and determining the precise location of anomalies in various audio applications.