Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production.

Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.

High-fidelity continuum modeling predicts avian voiced sound production.

Voiced sound production is the primary form of acoustic communication in terrestrial vertebrates, particularly birds and mammals, including humans. Developing a causal physics-based model that ultimately links descending vocal motor control to tissue vibration and sound requires embodied approaches that include realistic representations of voice physiology. Here, we first implement and then experimentally test a high-fidelity three-dimensional (3D) continuum model for voiced sound production in birds. Driven by individual-based physiologically quantifiable inputs, combined with noninvasive inverse methods for tissue material parameterization, our model accurately predicts observed key vibratory and acoustic performance traits. These results demonstrate that realistic models lead to accurate predictions and support the continuum model approach as a critical tool toward a causal model of voiced sound production.

Quantifying syringeal dynamics in vitro using electroglottography.

The complex and elaborate vocalizations uttered by many of the 10,000 extant bird species are considered a major driver in their evolutionary success, warranting study of the underlying mechanisms of vocal production. Additionally, birdsong has developed into a highly productive model system for vocal imitation learning and motor control, where, in contrast to humans, we have experimental access to the entire neuromechanical control loop. In human voice production, complex laryngeal geometry, vocal fold tissue properties, airflow and laryngeal musculature all interact to ultimately control vocal fold kinematics. Quantifying vocal fold kinematics is thus critical to understanding neuromechanical control of voiced sound production, but in vivo imaging of vocal fold kinematics in birds is experimentally challenging. Here, we adapted and tested electroglottography (EGG) as a novel tool for examining vocal fold kinematics in the avian vocal organ, the syrinx. We furthermore imaged and quantified syringeal kinematics in the pigeon (Columba livia) syrinx with unprecedented detail. Our results show that EGG signals predict (1) the relative amount of contact between the avian equivalent of vocal folds and (2) essential parameters describing vibratory kinematics, such as fundamental frequency, and timing of syringeal opening and closing events. As such, EGG provides novel opportunities for measuring syringeal vibratory kinematic parameters in vivo Furthermore, the opportunity for imaging syringeal vibratory kinematics from multiple planar views (horizontal and coronal) simultaneously promotes birds as an excellent model system for studying kinematics and control of voiced sound production in general, including in humans and other mammals.