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Animals produce vocalizations that range in complexity from a single repeated call to hundreds of unique vocal elements patterned in sequences unfolding over hours. Characterizing complex vocalizations can require considerable effort and a deep intuition about each species' vocal behavior. Even with a great deal of experience, human characterizations of animal communication can be affected by human perceptual biases. We present a set of computational methods for projecting animal vocalizations into low dimensional latent representational spaces that are directly learned from the spectrograms of vocal signals. We apply these methods to diverse datasets from over 20 species, including humans, bats, songbirds, mice, cetaceans, and nonhuman primates. Latent projections uncover complex features of data in visually intuitive and quantifiable ways, enabling high-powered comparative analyses of vocal acoustics. We introduce methods for analyzing vocalizations as both discrete sequences and as continuous latent variables. Each method can be used to disentangle complex spectro-temporal structure and observe long-timescale organization in communication.

Autism spectrum disorder (ASD) is characterised by social communication difficulties. These difficulties have been mainly explained by cognitive, motivational, and emotional alterations in ASD. The communication difficulties could, however, also be associated with altered sensory processing of communication signals. Here, we assessed the functional integrity of auditory sensory pathway nuclei in ASD in three independent functional magnetic resonance imaging experiments. We focused on two aspects of auditory communication that are impaired in ASD: voice identity perception, and recognising speech‐in‐noise. We found reduced processing in adults with ASD as compared to typically developed control groups (pairwise matched on sex, age, and full‐scale IQ) in the central midbrain structure of the auditory pathway (inferior colliculus [IC]). The right IC responded less in the ASD as compared to the control group for voice identity, in contrast to speech recognition. The right IC also responded less in the ASD as compared to the control group when passively listening to vocal in contrast to non‐vocal sounds. Within the control group, the left and right IC responded more when recognising speech‐in‐noise as compared to when recognising speech without additional noise. In the ASD group, this was only the case in the left, but not the right IC. The results show that communication signal processing in ASD is associated with reduced subcortical sensory functioning in the midbrain. The results highlight the importance of considering sensory processing alterations in explaining communication difficulties, which are at the core of ASD. Voice processing is an evolutionary preserved process and voice‐specific brain responses develop early in human life. Impaired voice processing is a characteristic behavioural symptom in autism, a neurodevelopmental condition associated with impaired social communication. Here, we show, in three neuroimaging experiments, that adults with autism have reduced voice processing in the central midbrain structure of the subcortical auditory pathway. The results show that communication signal processing in autism is associated with reduced subcortical sensory functioning in the midbrain, highlighting the importance of considering sensory processing alterations in explaining communication difficulties.

The study of non-human animals’ communication systems generally relies on the transcription of vocal sequences using a finite set of discrete units. This set is referred to as a vocal repertoire, which is specific to a species or a sub-group of a species. When conducted by human experts, the formal description of vocal repertoires can be laborious and/or biased. This motivates computerised assistance for this procedure, for which machine learning algorithms represent a good opportunity. Unsupervised clustering algorithms are suited for grouping close points together, provided a relevant representation. This paper therefore studies a new method for encoding vocalisations, allowing for automatic clustering to alleviate vocal repertoire characterisation. Borrowing from deep representation learning, we use a convolutional auto-encoder network to learn an abstract representation of vocalisations. We report on the quality of the learnt representation, as well as of state of the art methods, by quantifying their agreement with expert labelled vocalisation types from 8 datasets of other studies across 6 species (birds and marine mammals). With this benchmark, we demonstrate that using auto-encoders improves the relevance of vocalisation representation which serves repertoire characterisation using a very limited number of settings. We also publish a Python package for the bioacoustic community to train their own vocalisation auto-encoders or use a pretrained encoder to browse vocal repertoires and ease unit wise annotation.

Vocalizations are a pervasive feature of nonhuman primate social life, yet we know surprisingly little about their function. We review studies supporting the hypothesis that many primate vocalizations function to facilitate social interactions by reducing uncertainty about the signaler’s intentions and likely behavior. Such interactions help to establish and maintain the social bonds that increase reproductive success. Compared with humans, songbirds, and a few other mammals, primates have small vocal repertoires that show little acoustic modification during development. However, their ability to modify call usage is extensive and tuned to variation in the social context, including the historical relationship between caller and listener and the caller’s assessment of how a listener is likely to respond. We suggest parallels between the decision to vocalize and neurophysiological studies of other, nonvocal social decisions between interacting monkeys. The selective factors driving the early stages of language evolution may have come from the need to make decisions about when and how to call within the context of social challenges.

Increases in the scale and complexity of behavioral data pose an increasing challenge for data analysis. A common strategy involves replacing entire behaviors with small numbers of handpicked, domain-specific features, but this approach suffers from several crucial limitations. For example, handpicked features may miss important dimensions of variability, and correlations among them complicate statistical testing. Here, by contrast, we apply the variational autoencoder (VAE), an unsupervised learning method, to learn features directly from data and quantify the vocal behavior of two model species: the laboratory mouse and the zebra finch. The VAE converges on a parsimonious representation that outperforms handpicked features on a variety of common analysis tasks, enables the measurement of moment-by-moment vocal variability on the timescale of tens of milliseconds in the zebra finch, provides strong evidence that mouse ultrasonic vocalizations do not cluster as is commonly believed, and captures the similarity of tutor and pupil birdsong with qualitatively higher fidelity than previous approaches. In all, we demonstrate the utility of modern unsupervised learning approaches to the quantification of complex and high-dimensional vocal behavior.

We describe the individual and combined use of vocalizations and gestures in wild chimpanzees. The rate of gesturing peaked in infancy and, with the exception of the alpha male, decreased again in older age groups, while vocal signals showed the opposite pattern. Although gesture-vocal combinations were relatively rare, they were consistently found in all age groups, especially during affiliative and agonistic interactions. Within behavioural contexts rank (excluding alpha-rank) had no effect on the rate of male chimpanzees' use of vocal or gestural signals and only a small effect on their use of combination signals. The alpha male was an outlier, however, both as a prolific user of gestures and recipient of high levels of vocal and gesture-vocal signals. Persistence in signal use varied with signal type: chimpanzees persisted in use of gestures and gesture-vocal combinations after failure, but where their vocal signals failed they tended to add gestural signals to produce gesture-vocal combinations. Overall, chimpanzees employed signals with a sensitivity to the public/private nature of information, by adjusting their use of signal types according to social context and by taking into account potential out-of-sight audiences. We discuss these findings in relation to the various socio-ecological challenges that chimpanzees are exposed to in their natural forest habitats and the current discussion of multimodal communication in great apes.

Mice emit ultrasonic vocalizations (USVs) that communicate socially relevant information. To detect and classify these USVs, here we describe VocalMat. VocalMat is a software that uses image-processing and differential geometry approaches to detect USVs in audio files, eliminating the need for user-defined parameters. VocalMat also uses computational vision and machine learning methods to classify USVs into distinct categories. In a data set of >4000 USVs emitted by mice, VocalMat detected over 98% of manually labeled USVs and accurately classified ≈86% of the USVs out of 11 USV categories. We then used dimensionality reduction tools to analyze the probability distribution of USV classification among different experimental groups, providing a robust method to quantify and qualify the vocal repertoire of mice. Thus, VocalMat makes it possible to perform automated, accurate, and quantitative analysis of USVs without the need for user inputs, opening the opportunity for detailed and high-throughput analysis of this behavior.

Research over the last 20 years has shed important light on the vocal behaviour of our closest living relatives, bonobos and chimpanzees, but mostly relies on qualitative vocal repertoires, for which quantitative validations are absent. Such data are critical for a holistic understanding of a species` communication system and unpacking how these systems compare more broadly with other primate and non-primate species. Here we make key progress by providing the first quantitative validation of a Pan vocal repertoire, specifically for wild bonobos. Using data comprising over 1500 calls from 53 adult individuals collected over 33 months, we employ machine-learning-based random forest analyses and describe 11 acoustically distinguishable call types. We discuss issues associated with resolving vocal repertoires from wild data in great apes and highlight potential future approaches to further capture the complexity and gradedness of the bonobo vocal system.

During development, early-life stress (ELS) impairs cognition, learning, and emotional regulation, in part by disrupting neural circuitry in regions underlying these higher-order functions. In addition, our recent work indicates that ELS also alters simple sensory perception: ELS impaired auditory perception and neural encoding of short gaps in sounds, which are essential for vocal communication. The combination of higher-order and basic sensory disruption suggests that ELS is likely to affect both the perception and interpretation of communication signals. We tested this hypothesis by measuring behavioral responses to conspecific vocalizations (those emitted by other gerbils) in ELS and untreated Mongolian gerbils. Because stress effects often differ by sex, we separately examined females and males. To induce ELS, pups were intermittently maternally separated and restrained from post-natal days (P) 9–24, a time window when the auditory cortex is most sensitive to external disruption. We measured the approach responses of juvenile (P31–32) gerbils to two types of conspecific vocalizations: an alarm call, which is emitted to alert other gerbils of a potential threat, and the prosocial contact call, which is emitted near familiar gerbils, especially after separation. Control males, Control females, and ELS females approached a speaker emitting pre-recorded alarm calls, while ELS males avoided this source, suggesting that ELS affects the response to alarm calls in male gerbils. During playback of the pre-recorded contact call, Control females and ELS males avoided the sound source, while Control males neither approached nor avoided, and ELS females approached the sound. These differences cannot be accounted for by changes in locomotion or baseline arousal. However, ELS gerbils slept more during playback, suggesting that ELS may reduce arousal during vocalization playback. Further, male gerbils made more errors than females on a measure of working memory, but the sex difference of cognition in this context may stem from novelty aversion rather than impaired memory. These data indicate that ELS influences behavioral responses to ethologically relevant communication sounds in a sex-specific manner, and are among the first to demonstrate an altered response to auditory stimuli following ELS. Such changes may arise from differences in auditory perception, cognition, or a combination of factors, and suggest that ELS may affect auditory communication in human adolescents.

The efficiency of motor skill acquisition is age-dependent, making it increasingly challenging to learn complex manoeuvres later in life. Zebra finches, for instance, acquire a complex vocal motor programme during a developmental critical period after which the learned song is essentially impervious to modification. Although inhibitory interneurons are implicated in critical period closure, it is unclear whether manipulating them can reopen heightened motor plasticity windows. Using pharmacology and a cell-type specific optogenetic approach, we manipulated inhibitory neuron activity in a premotor area of adult zebra finches beyond their critical period. When exposed to auditory stimulation in the form of novel songs, manipulated birds added new vocal syllables to their stable song sequence. By lifting inhibition in a premotor area during sensory experience, we reintroduced vocal plasticity, promoting an expansion of the syllable repertoire without compromising pre-existing song production. Our findings provide insights into motor skill learning capacities, offer potential for motor recovery after injury, and suggest avenues for treating neurodevelopmental disorders involving inhibitory dysfunctions. Motor skill acquisition decreases with age, as seen in zebra finches whose songs become fixed after a critical period. By manipulating inhibitory neurons in adult finches, the authors restored vocal plasticity, allowing the birds to learn new syllables without affecting existing ones.