Model-based unsupervised segmentation of birdcalls from field recordings
Anshul Thakur, P.K. Rajan · 2016
In this paper, we describe an unsupervised, species independent method to segment birdcalls from the background in bio-acoustic recordings. The method follows a two-pass approach. An initial segmentation is performed utilizing K-means clustering. This provides labels to train Gaussian mixture acoustic models, which are built using Mel frequency cepstral coefficients. Using the acoustic models, the segmentation is refined further to classify each short-time frame as belonging either to the background or to call-activity. Different features, namely short-time energy, Fourier transform phase-based entropy and inverse spectral flatness (ISF) are evaluated within the framework of the proposed method. Our experiments with real field recordings on two datasets reveal that the ISF reliably provides better segmentation performance when compared to the other two features.