System and methods of novelty detection using non-parametric machine learning

Abstract

In general, a system and method consistent with the present disclosure allows for non-parametric modeling of audio data by advantageously utilizing a feature space of training vectors that is one-dimensional. A novelty detector consistent with the present disclosure may capture a plurality of audio samples and convert the same into a time-frequency domain pattern to establish a baseline sound signature using a statistical approach. A plurality of monitoring nodes may be associated with one or more frequencies represented within the time-frequency domain pattern. Each node may then compare subsequently captured time-frequency domain patterns to detect values which exceed a so-called “normal” threshold, with the threshold being dynamically derived based on the baseline sound signature in some embodiments. In the event a predetermined number of nodes detect a novelty in the sound signature, alerts may be issued to users/technicians.

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