@book {57373,
	title = {Deep Representation Learning for Orca Call Type Classification},
	year = {2019},
	abstract = {<p>Marine mammals produce a wide variety of vocalizations. There is a growing need for robust automatic classification methods especially in noisy underwater environments in order to access large amounts of bioacoustic signals and to replace tedious and error prone human perceptual classification. In case of the northern resident killer whale (Orcinus orca), echolocation clicks, whistles, and pulsed calls make up its vocal repertoire. Pulsed calls are the most intensively studied type of vocalization. In this study we propose a hybrid call type classification approach outperforming our previous work on supervised call type classification consisting of two components: (1) deep representation learning of killer whale sounds by investigating various autoencoder architectures and data corpora and (2) subsequent supervised training of a ResNet18 call type classifier on a much smaller dataset by using the pre-trained representations. The best semi-supervised trained classification model achieved a test accuracy of 96\% and a mean test accuracy of 94\% outperforming our previous work by 7\% points.</p>
},
	keywords = {bioacoustics, call type, classification, Deep learning, Killer whale, Orca, Representation learning},
	isbn = {978-3-030-27946-2},
	doi = {10.1007/978-3-030-27947-910.1007/978-3-030-27947-9_23},
	url = {http://link.springer.com/10.1007/978-3-030-27947-9},
	author = {Bergler, Christian and Schmitt, Manuel and Cheng, Rachael Xi and Schr{\"o}ter, Hendrik and Maier, Andreas and Barth, Volker and Weber, Michael and N{\"o}th, Elmar},
	editor = {Ek{\v s}tein, Kamil}
}
@article {47715,
	title = {Call Repertoire of the Sahamalaza Sportive Lemur, Lepilemur sahamalazensis},
	journal = {International Journal of Primatology},
	volume = {36},
	year = {2015},
	month = {Jan-06-2015},
	pages = {647 - 665},
	abstract = {The acoustic structure of primate loud calls can be used as a powerful, inexpensive, and noninvasive tool for intra- and interspecific comparative analyses, reconstruction of phylogeny, and primate surveys. Despite the range of possibilities offered by acoustic analysis, only few studies so far have focused on quantitative descriptions of the acoustic structure of primate loud call repertoires. Here we aimed to assess the vocal repertoire of the solitary Sahamalaza sportive lemur, Lepilemur sahamalazensis, and to investigate potential communication functions. We recorded every sportive lemur vocalization we heard during 1000 h of nocturnal observations of eight collared individuals, as well as opportunistic searches in the Ankarafa Forest, Sahamalaza Peninsula in northwest Madagascar. In addition, we used playback experiments with four call types to clarify call function. We measured both temporal and spectral properties to describe calls quantitatively and used cross-validated discriminant function analysis to validate call types that we identified from a preliminary qualitative inspection of the spectrograms of 107 calls. We identified six distinct loud call types with the possibility of a seventh call type, with six loud call types similar to those of Lepilemur edwardsi and two loud call types similar to those of four other sportive lemur species. The described call types most likely function in mate advertisement, offspring care, and territorial defense. Future studies of loud calling of the Sahamalaza sportive lemur are needed to clarify if certain call types are sex specific and if loud calls could be used for recognition of individuals to enable noninvasive density measurements and species monitoring.},
	keywords = {bioacoustics, Call function, call type, communication, Lepilemur},
	issn = {0164-0291},
	doi = {10.1007/s10764-015-9846-0},
	url = {http://link.springer.com/10.1007/s10764-015-9846-0},
	author = {Seiler, Melanie and Schwitzer, Christoph}
}
