![]() ![]() Many bioacoustic studies generated an enormous amount of data, which made this manual review process at best inefficient, and at worst impossible to accomplish.įor decades, scientists have worked to automate the process of detecting and classifying sounds into categories or types. Output from a long-duration sound had to be spliced together (see Chap. Before the advent of digital signal-analysis, data were analyzed while enduring the acrid smell of etched Kay Sona-Graph paper and piles of 8-s printouts removed from a spinning recording drum littering laboratory tables and floors. In the past, detection and classification tasks were performed by an experienced bioacoustician who listened to the sounds and visually reviewed spectrographic displays (e.g., for birds by Baptista and Gaunt 1997 chipmunks by Gannon and Lawlor 1989 baleen whales by Stafford et al. Some mammal species produce distinctive, stereotyped sounds (e.g., chipmunks, dogs, and blue whales), while others produce signals with high variability (e.g., mimicking birds, primates, and dolphins).īecause animals produce so many different types of sounds, developing algorithms to detect, recognize, and classify a wide range of acoustic signals can be challenging. Song birds and some species of baleen whales arrange individual sounds into patterns called song and repeat these patterns for hours or days. For example, the repertoire of marine mammal acoustic signals includes broadband echolocation clicks as short as 10 μs in duration and with energy up to 200 kHz, as well as narrowband tonal sounds as low as 10–20 Hz, lasting more than10 s. Animals produce many different types of sounds that span orders of magnitude along the dimensions of time, frequency, and amplitude. 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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