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Processing of Graded Signaling Systems

dc.contributor.advisorFischer, Julia Prof. Dr.
dc.contributor.authorWadewitz, Philip
dc.titleProcessing of Graded Signaling Systemsde
dc.contributor.refereeFischer, Julia Prof. Dr.
dc.description.abstractengVocal repertoires of nonhuman animals and especially of terrestrial mammals are often characterized by their relatively small size of innate vocal types which can show considerable variation in acoustic structure. To understand the proximate and ultimate causes that shape the structure of acoustic communication systems in animals, an objective characterization of the vocal repertoire of a given species is critical, as it provides the foundation for comparative analyses among individuals, populations, and taxa. The common approach to characterize vocal repertoires is by using unsupervised clustering algorithms to identify call types and to define a repertoire's size. Progress in the field has been hampered by a lack of standard in methodology which can lead to an arbitrary decision about the size of a species' repertoire. To investigate whether this difficulty is based on the used methodology or whether it is intrinsic to the acoustic structure of a given repertoire, the major aim of my dissertation was to investigate and advance the available methods in the field. To do so, I focused on three main aspects of a vocal repertoire analysis: (1) how is the analysis affected by the input parameters, i.e. the acoustic features that are used; (2) how can we quantify the acoustic variation within and between different vocal types; (3) what is the impact of data set composition, i.e. the call recordings that are being used in the analysis. In the first part of my thesis, I re-analyzed recordings from wild chacma baboons (Papio ursinus) to test the impact of the number and type of acoustic features that are included in the analysis. To do this, I constructed data sets with the same 912 call exemplars but with a varying number of acoustic features to describe these calls. To this end, I had three data sets with 9, 38, and 118 acoustic features as well as a data set with 19 factors derived from a principal component analysis. By comparing and validating the resulting classifications of two clustering algorithms, namely k-means and hierarchical Ward's clustering, I could show that the data sets with a higher number of acoustic features lead to better clustering results than data sets with only a few features. I further showed that factors are not suited to cluster the chacma baboon's calls. None of the applied clustering algorithms gave strong support to a specific cluster solution. Since there was substantial acoustic variation within and between the different call types, I applied an approach based on fuzzy logic that we developed to describe the gradation within vocal repertoires and which provides a quantitative description of the gradation within the chacma baboon's repertoire. To investigate the impact of potential evolutionary forces that shape a species' communication system, comparative studies that quantify the differences in these systems between different species are necessary. In the second part of my thesis, I strove towards such a quantitative comparison by systematically comparing the vocal repertoire of the chacma baboon with the vocal repertoire of the Barbary macaque, Macaca sylvanus. I quantified the gradation within and between different call types of both species with an extended version of the fuzzy clustering approach that was used to characterize the chacma baboon's repertoire in the first part of this thesis. The analysis confirmed the findings of previous studies by showing that the repertoire of the Barbary macaque exhibits a significant larger amount of gradation within and between different call types. An important aspect of this method is that it allows the quantification of gradation irrespective of the number of call types by circumventing the problem to settle on one cluster solution when several solutions are largely equivalent. In the third part of my thesis, I investigated the influence of the data set composition that is used for the analysis of vocal repertoires. Specifically, I was interested in the effects of size- and arousal based differences in the recorded animals and their impact on clustering results. The differences in body size and arousal were simulated with a software-based model that simulates muscle characteristics of the larynx and vocal tract anatomy. With this model I created pseudo repertoires of three distinct baboon call types that varied in subglottic pressure levels (as a proxy of arousal-based differences) and vocal fold and vocal tract characteristics (size-based differences). The preliminary results show that whereas differences in subglottic pressure levels had a minor impact on the characteristics of vocal repertoires and all three call types can be clearly separated from each other, differences in body size can hamper classification and characterization of call types. In conclusion, I investigated several aspects that have to be taken into account when analyzing vocal repertoires. The composition of the data sets as well as the selection of acoustic features that are used in the analysis can both have a profound effect on the classification outcome and on cluster determination. To overcome the often arbitrary decision about a species repertoire size I developed a method that is useful to describe the gradation within and between different call types over several cluster solutions and therefore circumvents the problem to settle on one specific solution. In addition, the method allows a systematic comparison of different species' vocal repertoires, a prerequisite to investigate potential driving forces in signal
dc.contributor.coRefereeWolf, Fred Prof. Dr.
dc.subject.engVocal repertoirede
dc.subject.engChacma baboonsde
dc.subject.engBarbary macaquesde
dc.subject.engFuzzy clusteringde
dc.subject.engAcoustic communicationde
dc.affiliation.instituteGöttinger Graduiertenschule für Neurowissenschaften, Biophysik und molekulare Biowissenschaften (GGNB)de
dc.subject.gokfullBiologie (PPN619462639)de

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