Researchers from the University of Amsterdam and Leiden University have discovered that humans and songbirds use markedly different strategies to recognize sequences of complex sounds. The findings sh...

Löschner and Hage report that marmoset vocalizations have a quantized logic, being constructed of a hierarchically nested, rhythmically gated set of vocal motor elements.

Vocal signals are used to convey information in environments and over distances where signals of other modalities would fall short. Some of the information that they contain concerns the individual...

Categorical rhythmic patterns are fundamental features found in language and music across cultures. In humans, these rhythmic patterns occur in both vocal production and perceptual preference, highlighting a link between rhythmic production and preference. Categorical rhythmic patterns have recently been identified in animal vocalisations too, but whether rhythm production is linked with rhythmic preferences in non-human animals remains unclear. Within this context, zebra finches (Taeniopygia castanotis) are of particular interest, as both sexes develop a song preference, and their song has been suggested to exhibit an isochronous temporal structure. Here, we tested for the first time whether zebra finches prefer songs containing discrete rhythmic patterns, using operant preference tests with either two or four rhythmic patterns. Our results show that no categorical rhythmic pattern was consistently preferred across the population, although responses varied among individuals. These findings indicate that categorical rhythmic patterns are unlikely to be a primary driver of song preference in zebra finches. Together with previous work in other vocal-learning birds, our results suggest that preferences for categorical rhythmic patterns may not be universal and could instead reflect species-specific features of vocal displays rather than the rhythmic categories characteristic of human music.

Vocal communication is a conserved vertebrate social behavior that begins at birth with the cries of infants. These neonatal vocal signals vary across contexts to elicit parental care that matches inf...

Deep learning is transforming the study of animal sound, enabling the automated identification of species, individuals, behaviors, and ecological patterns from large collections of recordings. While bioacoustic machine-learning models are growing more powerful, many biologists—ecologists, behavioral scientists, conservationists—and others working with acoustic data feel unprepared to navigate the computational workflows required to implement them. This article presents practical guidelines covering the full lifecycle of bioacoustic machine learning, including problem definition, data sourcing and annotation, model training, evaluation, deployment, reproducibility, and ethical considerations. Rather than providing a linear checklist, the guidelines outline an iterative framework for building science-led workflows, leveraging transfer learning and open-source tools, evaluating models based on the real-world cost of errors, and addressing domain shift under variable field conditions. Ultimately, this workflow demystifies the software-engineering process, providing a low-barrier and reproducible pathway for researchers applying machine learning to animal sound.

Mapping nature involves generating and analyzing massive streams of data in all shapes and forms. Consequently, biology and computer science are becoming increasingly intertwined.

Deep learning is transforming the study of animal sound, enabling the automated identification of species, individuals, behaviors, and ecological patterns from large collections of recordings. While b...

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The acoustic niche hypothesis posits that species partition acoustic space by frequency and time to avoid signal masking, and that any local loss of species leaves detectable gaps in co-evolved sound...

PhD candidate in Acoustic ecology of diving seabirds

Many social animals exhibit fission-fusion dynamics, where group members split into subgroups and come back together. Studying these dynamics can reveal how individuals weigh the costs and benefits of sociality. Most studies infer the drivers of fission-fusion dynamics by examining subgroup association patterns, yet different underlying processes can produce similar patterns. Here we take a fundamentally different approach by focusing on subgrouping events themselves, where decision-making plays out in real time. Using multi-sensor tracking collars, we collected simultaneous movement and vocalisation data from all group members within two wild white-nosed coati groups. We developed analytical tools to extract and characterise fission and fusion events, thus establishing a framework for quantifying the spatiotemporal dynamics of these events and the use of vocalisations before, during, and after them. We found that fissions typically occurred when groups were initially stationary, indicating that splitting does not result from loss of coordination while moving. Subgrouping was associated with reduced aggression, yet aggressive vocalisations did not precede splits, supporting the hypothesis that subgrouping is a pre-emptive mechanism to manage within-group conflict. Contact calls in moving subgroups increased before fissions and fusions, indicating that vocal communication is key to the coordination of these collective movement decisions.

For many group living species, vocal signals are a vital form of communication needed to coordinate social behaviours. Investigating these processes first requires a detailed description of the species' vocal repertoire. White-nosed coatis (Nasua narica), which forage and move together in forest habitats, are thought to rely on vocalisations to coordinate movements and maintain group cohesion. However, quantitative studies of their vocal repertoire are lacking. We examined the vocal repertoire of white-nosed coatis from wild populations in Panama and Arizona, USA, to gain a more comprehensive view of their calling behaviours. By combining traditional acoustic analyses with an unsupervised approach based on spectrogram structure, we characterised the diversity of calls in this species and described the temporal and structural features of their vocalisations. We identified 19 call types, with some of these calls emitted in multi-syllable call sequences or in fast succession. We found variability in call rates among group members, which may be driven by differences in social status within the group. In addition, our results indicate that white-nosed coatis likely have individually-recognisable vocalisations. This study provides a foundational description of the white-nosed coati vocal repertoire, laying the groundwork for future research on vocal communication in this species.

Neurons in the bat auditory cortex encode upcoming vocalizations before they are emitted, distinguishing call category and temporal structure. These findings reveal predictive, vocalization-specific s...

Abstract. Socially mediated vocal plasticity, such as vocal accommodation, is an important mechanism for maintaining social cohesion and coordination in gr

Monitoring demography is key to understanding how species respond to environmental change; however, in estimating demographic rates it is important to consider any potential biases, for example throu...

Researchers working with Cerulean Warblers are developing a tool that could allow scientists to recognise returning birds by voice, reducing the need to recapture and re-identify them

Combining a computational framework and optogenetic and chemogenetic manipulations within and downstream of the cortico-basal ganglia circuit identifies the specific cortico-basal ganglia synapse...

Abstract. Understanding one another in daily communication depends on predicting language from prior discourse context and visual signals, such as co-speech gestures. However, it remains unclear how d...