Dissimilarity-based classification for bioacoustic monitoring of bird species

The wealth of biodiversity is difficult to estimate because field inspections are exhausting and expensive. However, automatic monitoring systems can be a feasible option to partially overcome such a drawback. In this study, we present a process of bioacoustic recognition based on digital signal processing and pattern recognition techniques. On top of representations extracted from waveforms and spectra as well as computed by dissimilarities between pairs of them, we build classifiers for identifying 11 species in a data set of bird sounds recorded in the Colombian mountains. Results show that time-varying representations are a particularly good option for characterizing signals in this problem.