02507nas a2200253   4500008004100000022001400041245012300055210006900178260001600247520173100263653002701994653000902021653001002030653001702040653002102057100001902078700001502097700001602112700001202128700001702140700001602157700002402173856005602197       2019                        eng d  a1435-944800aBats increase vocal amplitude and decrease vocal complexity to mitigate noise interference during social communication0 aBats increase vocal amplitude and decrease vocal complexity to m  cOct-01-20193 a<p>Natural background noises are common in the acoustic environments in which most organisms have evolved. Therefore, the vocalization and sound perception systems of vocal animals are inherently equipped to overcome natural background noise. Human-generated noises, however, pose new challenges that can hamper audiovocal communication. The mechanisms animals use to cope with anthropogenic noise disturbances have been extensively explored in a variety of taxa. Bats emit echolocation pulses primarily to orient, locate and navigate, while social calls are used to communicate with conspecifics. Previous studies have shown that bats alter echolocation pulse parameters in response to background noise interference. In contrast to high-frequency echolocation pulses, relatively low-frequency components within bat social calls overlap broadly with ambient noise frequencies. However, how bats structure their social calls in the presence of anthropogenic noise is not known. Here, we hypothesized that bats leverage vocal plasticity to facilitate vocal exchanges within a noisy environment. To test this hypothesis, we subjected the Asian particolored bat, Vespertilio sinensis, to prerecorded traffic noise. We observed a significant decrease in vocal complexity (i.e., an increased frequency of monosyllabic calls) in response to traffic noise. However, an increase in the duration and frequency of social calls, as have been observed in other species, was not evident. This suggests that signal simplification may increase communication efficacy in noisy environments. Moreover, V. sinensis also increased call amplitude in response to increased traffic noise, consistent with the predictions of the Lombard effect.</p>
10aacoustic communication10abats10aNoise10aSocial calls10avocal plasticity1 aJiang, Tinglei1 aGuo, Xiong1 aLin, Aiqing1 aWu, Hui1 aSun, Congnan1 aFeng, Jiang1 aKanwal, Jagmeet, S.  uhttp://link.springer.com/10.1007/s10071-018-01235-002344nas a2200229   4500008004100000022001400041245016000055210006900215260001600284300001400300490000700314520158400321653002301905653001901928653001201947653001901959653001301978653001701991100002002008700003102028856005502059       2016                        eng d  a1435-944800aThe vocal repertoire of the domesticated zebra finch: a data-driven approach to decipher the information-bearing acoustic features of communication signals0 avocal repertoire of the domesticated zebra finch a datadriven ap  cJan-03-2016  a285 - 3150 v193 a<p>Although a universal code for the acoustic features of animal vocal communication calls may not exist, the thorough analysis of the distinctive acoustical features of vocalization categories is important not only to decipher the acoustical code for a specific species but also to understand the evolution of communication signals and the mechanisms used to produce and understand them. Here, we recorded more than 8000 examples of almost all the vocalizations of the domesticated zebra finch, Taeniopygia guttata: vocalizations produced to establish contact, to form and maintain pair bonds, to sound an alarm, to communicate distress or to advertise hunger or aggressive intents. We characterized each vocalization type using complete representations that avoided any a priori assumptions on the acoustic code, as well as classical bioacoustics measures that could provide more intuitive interpretations. We then used these acoustical features to rigorously determine the potential information-bearing acoustical features for each vocalization type using both a novel regularized classifier and an unsupervised clustering algorithm. Vocalization categories are discriminated by the shape of their frequency spectrum and by their pitch saliency (noisy to tonal vocalizations) but not particularly by their fundamental frequency. Notably, the spectral shape of zebra finch vocalizations contains peaks or formants that vary systematically across categories and that would be generated by active control of both the vocal organ (source) and the upper vocal tract (filter).</p>
10aAcoustic signature10aclassification10aMeaning10aRegularization10aSongbird10avocalization1 aElie, Julie, E.1 aTheunissen, Frédéric, E.  uhttp://link.springer.com/10.1007/s10071-015-0933-6