Clustering and visualization of long-duration audio recordings for rapid exploration avian surveys

Acoustic recordings have been shown to be an effective way to conduct avian species surveys, whereby a trained expert listens to the audio and records observations, a task that can be very time consuming. In practice, most identification of species are first made by visual inspection of the spectrogram, with listening then performed for verification. This paper presents an approach for a surveyor to rapidly scan long duration recordings of environmental audio by automatically filtering parts with low activity and repetitions of the same call types. Recordings are segmented into fixed-length one-second non-overlapping clips. A classifier filters segments of low activity using features that are robust to different levels of background noise. The non-silent segments are then clustered using a feature representation derived from Time-domain Cepstral Coefficients, calculated from the discrete Fourier transform of downsampled spectrogram rows. This time-invariant feature representation allows for arbitrary segmentation, which is advantageous because segmentation of complex audio soundscapes into individual events is difficult and prone to errors. A visualization tool displays a representative segment from each cluster, grouped hierarchically, allowing an ecological researcher to very rapidly visually scan through the entire variety of audio events that occurred throughout the long recording, without wasting time on silent portions of the recording or repetitions of the same call-type. This tool provides functionality missing from both time-consuming audio players and black-box pattern recognizers, allowing conservation scientists to visually explore the entirety of their recordings