Audio segmentation using Flattened Local Trimmed Range for ecological acoustic space analysis

The acoustic space in a given environment is filled with footprints arising from three processes: biophony, geophony and anthrophony. Bioacoustic research using passive acoustic sensors can result in thousands of recordings. An important component of processing these recordings is to automate signal detection. In this paper, we describe a new spectrogram-based approach for extracting individual audio events. Spectrogram-based audio event detection (AED) relies on separating the spectrogram into background (i.e., noise) and foreground (i.e., signal) classes using a threshold such as a global threshold, a per-band threshold, or one given by a classifier. These methods are either too sensitive to noise, designed for an individual species, or require prior training data. Our goal is to develop an algorithm that is not sensitive to noise, does not need any prior training data and works with any type of audio event. To do this, we propose: (1) a spectrogram filtering method, the Flattened Local Trimmed Range (FLTR) method, which models the spectrogram as a mixture of stationary and non-stationary energy processes and mitigates the effect of the stationary processes, and (2) an unsupervised algorithm that uses the filter to detect audio events. We measured the performance of the algorithm using a set of six thoroughly validated audio recordings and obtained a sensitivity of 94% and a positive predictive value of 89%. These sensitivity and positive predictive values are very high, given that the validated recordings are diverse and obtained from field conditions. The algorithm was then used to extract audio events in three datasets. Features of these audio events were plotted and showed the unique aspects of the three acoustic communities. Subjects Bioinformatics, Computational Biology