System and method for anomaly detection in unlabeled collections of audio recording

Abstract

In some implementations, the device may include receiving a first and second audio dataset. In addition, the device may generate a first, a second, a third, and a fourth audio sample. Moreover, the device may include determining a level of similarity between the first and second audio samples. Also, the device may include combining the first and second audio samples into an audio pair. Further, the device may include training a machine learning model to map audio samples to a latent space visualization in view of time and the similarities between the first and second audio samples to yield a trained machine learning model. In addition, the device may include mapping, by the machine learning model, in the latent space visualization, the third and fourth audio samples, where placement of the third and fourth audio samples depends on the level of similarity between the third and fourth audio samples.