@article {57663, title = {Whistling shares a common tongue with speech: bioacoustics from real-time MRI of the human vocal tract}, year = {2019}, abstract = {

Most human communication is carried by modulations of the voice. However, a wide range of cultures has developed alternative forms of communication that make use of a whistled sound source. For example, whistling is used as a highly salient signal for capturing attention, and can have iconic cultural meanings such as the catcall, enact a formal code as in boatswain\&$\#$39;s calls or stand as a proxy for speech in whistled languages. We used real-time magnetic resonance imaging to examine the muscular control of whistling to describe a strong association between the shape of the tongue and the whistled frequency. This bioacoustic profile parallels the use of the tongue in vowel production. This is consistent with the role of whistled languages as proxies for spoken languages, in which one of the acoustical features of speech sounds is substituted with a frequency-modulated whistle. Furthermore, previous evidence that non-human apes may be capable of learning to whistle from humans suggests that these animals may have similar sensorimotor abilities to those that are used to support speech in humans.

}, keywords = {communication, evolution, magnetic resonance imaging, speech, tongue, whistle}, doi = {10.1098/rspb.2019.1116}, url = {https://royalsocietypublishing.org/doi/10.1098/rspb.2019.1116https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2019.1116}, author = {Belyk, Michel and Schultz, Benjamin G. and Correia, Joao and Beal, Deryk S. and Kotz, Sonja A.} } @article {47717, title = {On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, volume = {25}, year = {2017}, month = {Jan-06-2017}, pages = {1193 - 1206}, abstract = {

We introduce a novel approach to studying animal behavior and the context in which it occurs, through the use of microphone backpacks carried on the backs of individual free-flying birds. These sensors are increasingly used by animal behavior researchers to study individual vocalizations of freely behaving animals, even in the field. However, such devices may record more than an animal\&$\#$39;s vocal behavior, and have the potential to be used for investigating specific activities (movement) and context (background) within which vocalizations occur. To facilitate this approach, we investigate the automatic annotation of such recordings through two different sound scene analysis paradigms: A scene-classification method using feature learning, and an event-detection method using probabilistic latent component analysis. We analyze recordings made with Eurasian jackdaws (Corvus monedula) in both captive and field settings. Results are comparable with the state of the art in sound scene analysis; we find that the current recognition quality level enables scalable automatic annotation of audio logger data, given partial annotation, but also find that individual differences between animals and/or their backpacks limit the generalization from one individual to another. we consider the interrelation of \“scenes\” and \“events\” in this particular task, and issues of temporal resolution.

}, keywords = {acoustics, birds, context, event detection, image analysis, microphones, pattern recognition, speech, zoology}, issn = {2329-9290}, doi = {10.1109/TASLP.2017.2690565}, url = {http://ieeexplore.ieee.org/document/7933044/}, author = {Stowell, Dan and Gill, Lisa F.} }