![]() Classification provides a convenient method for comparing features, making systematic measurements, testing hypotheses, and performing statistical analyses.īioacousticians have categorized sounds produced by animals for decades, and new methods for classification continue to be developed (Horn and Falls 1996 Beeman 1998). Categorization also can make recognition of patterns easier and assist in understanding the ways in which biological systems work. For example, organisms can be classified based on biome, ecosystem, taxon, phylogeny, niche, demographic class, behavior type, etc., and this allows complex systems to be organized. Researchers have a natural tendency to classify biological systems into categories. Methods for evaluating the performance of automated tools are presented (i.e., receiver operating characteristics and precision-recall) and challenges with classifying animal sounds are discussed. In this chapter, we present software algorithms for automated signal detection (based on energy, Teager–Kaiser energy, spectral entropy, matched filtering, and spectrogram cross-correlation) as well as for signal classification (e.g., parametric clustering, principal component analysis, discriminant function analysis, classification trees, artificial neural networks, random forests, Gaussian mixture models, support vector machines, dynamic time-warping, and hidden Markov models). Advances in computer technology and the development of software for the automatic detection and classification of sounds have allowed bioacousticians to quickly find sounds in recordings, thus significantly reducing analysis time and enabling the analysis of larger datasets. Initially, researchers applied qualitative methods, such as listening and visually discerning sounds in spectrograms. Techniques for classification have evolved over time as technical capabilities have expanded. Classification of acoustic repertoires enables the identification of species, age, gender, and individual identity, correlations between sound types and behavior, the identification of changes in vocal behavior over time or in response to anthropogenic noise, comparisons between the repertoires of populations living in different geographic regions and environments, and the development of software tools for automated signal processing. Classification of the acoustic repertoires of animals into sound types is a useful tool for taxonomic studies, behavioral studies, and for documenting the occurrence of animals.
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