Acoustic allometry and vocal learning in mammals.
Ontology highlight
ABSTRACT:
SUBMITTER: Garcia M
PROVIDER: S-EPMC7423041 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:A fundamental issue in the evolution of communication is the degree to which signals convey accurate ("honest") information about the signaler. In bioacoustics, the assumption that fundamental frequency (f o) should correlate with the body size of the caller is widespread, but this belief has been challenged by various studies, possibly because larynx size and body size can vary independently. In the present comparative study, we conducted excised larynx experiments to investigate this hypothesis rigorously and explore the determinants of f o. Using specimens from eleven primate species, we carried out an inter-specific investigation, examining correlations between the minimum f o produced by the sound source, body size and vocal fold length (VFL). We found that, across species, VFL predicted minimum f o much better than body size, clearly demonstrating the potential for decoupling between larynx size and body size in primates. These findings shed new light on the diversity of primate vocalizations and vocal morphology, highlighting the importance of vocal physiology in understanding the evolution of mammal vocal communication.
| S-EPMC5585385 | biostudies-literature
Project description:Anthropogenic noise imposes novel selection pressures, especially on species that communicate acoustically. Many animals-including insects, frogs, whales and birds-produce sounds at higher frequencies in areas with low-frequency noise pollution. Although there is support for animals changing their vocalizations in real time in response to noise (i.e. immediate flexibility), other evolutionary mechanisms for animals that learn their vocalizations remain largely unexplored. We hypothesize that cultural selection for signal structures less masked by noise is a mechanism of acoustic adaptation to anthropogenic noise. We test this hypothesis by presenting nestling white-crowned sparrows (Zonotrichia leucophyrs) with less-masked (higher-frequency) and more-masked (lower-frequency) tutor songs either during playback of anthropogenic noise (noise-tutored treatment) or at a different time from noise playback (control treatment). As predicted, we find that noise-tutored males learn less-masked songs significantly more often, whereas control males show no copying preference, providing strong experimental support for cultural selection in response to anthropogenic noise. Further, noise-tutored males reproduce songs at higher frequencies than their tutor, indicating a distinct mechanism to increase signal transmission in a noisy environment. Notably, noise-tutored males achieve lower performance songs than their tutors, suggesting potential costs in a sexual selection framework.
| S-EPMC6191693 | biostudies-literature
Project description:ObjectiveTo detect unilateral vocal fold paralysis (UVFP) from voice recordings using an explainable model of machine learning.Study designCase series - retrospective with a control group.SettingTertiary care laryngology practice between 2009 to 2019.MethodsPatients with confirmed UVFP through endoscopic examination (N=77) and controls with normal voices matched for age and sex (N=77) were included. Two tasks were used to elicit voice samples: reading the Rainbow Passage and sustaining phonation of the vowel "a". The 88 extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) features were extracted as inputs for four machine learning models of differing complexity. SHAP was used to identify important features.ResultsThe median bootstrapped Area Under the Receiver Operating Characteristic Curve (ROC AUC) score ranged from 0.79 to 0.87 depending on model and task. After removing redundant features for explainability, the highest median ROC AUC score was 0.84 using only 13 features for the vowel task and 0.87 using 39 features for the reading task. The most important features included intensity measures, mean MFCC1, mean F1 amplitude and frequency, and shimmer variability depending on model and task.ConclusionUsing the largest dataset studying UVFP to date, we achieve high performance from just a few seconds of voice recordings. Notably, we demonstrate that while similar categories of features related to vocal fold physiology were conserved across models, the models used different combinations of features and still achieved similar effect sizes. Machine learning thus provides a mechanism to detect UVFP and contextualize the accuracy relative to both model architecture and pathophysiology.
| S-EPMC7836138 | biostudies-literature
Project description:Melatonin is a well-documented time-keeping hormone that can entrain an individual's physiology and behavior to the day-night cycle, though surprisingly little is known about its influence on the neural basis of social behavior, including vocalization. Male midshipman fish (Porichthys notatus) produce several call types distinguishable by duration and by daily and seasonal cycles in their production. We investigated melatonin's influence on the known nocturnal- and breeding season-dependent increase in excitability of the midshipman's vocal network (VN) that directly patterns natural calls. VN output is readily recorded from the vocal nerve as a 'fictive call'. Five days of constant light significantly increased stimulus threshold levels for calls electrically evoked from vocally active sites in the medial midbrain, supporting previous findings that light suppresses VN excitability, while 2-iodomelatonin (2-IMel; a melatonin analog) implantation decreased threshold. 2-IMel also increased fictive call duration evoked from medial sites as well as lateral midbrain sites that produced several-fold longer calls irrespective of photoregime or drug treatment. When stimulus intensity was incrementally increased, 2-IMel increased duration only at lateral sites, suggesting that melatonin action is stronger in the lateral midbrain. For animals receiving 5 days of constant darkness, known to increase VN excitability, systemic injections of either of two mammalian melatonin receptor antagonists increased threshold and decreased duration for calls evoked from medial sites. Our results demonstrate melatonin modulation of VN excitability and suggest that social context-dependent call types differing in duration may be determined by neuro-hormonal action within specific regions of a midbrain vocal-acoustic network.
| S-EPMC3966919 | biostudies-literature
Project description:Speech impairments often emerge as one of the primary indicators of Parkinson's disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.
| S-EPMC10968564 | biostudies-literature
Project description:Valid, reliable biomarkers of depression severity and treatment response would provide new targets for clinical research. Noticeable differences in speech production between depressed and nondepressed patients have been suggested as a potential biomarker.One hundred five adults with major depression were recruited into a 4-week, randomized, double-blind, placebo-controlled research methodology study. An exploratory objective of the study was to evaluate the generalizability and repeatability of prior study results indicating vocal acoustic properties in speech may serve as biomarkers for depression severity and response to treatment. Speech samples, collected at baseline and study end point using an automated telephone system, were analyzed as a function of clinician-rated and patient-reported measures of depression severity and treatment response.Regression models of speech pattern changes associated with clinical outcomes in a prior study were found to be reliable and significant predictors of outcome in the current study, despite differences in the methodological design and implementation of the two studies. Results of the current study replicate and support findings from the prior study. Clinical changes in depressive symptoms among patients responding to the treatments provided also reflected significant differences in speech production patterns. Depressed patients who did not improve clinically showed smaller vocal acoustic changes and/or changes that were directionally opposite to treatment responders.This study supports the feasibility and validity of obtaining clinically important, biologically based vocal acoustic measures of depression severity and treatment response using an automated telephone system.
| S-EPMC3409931 | biostudies-literature
Project description:Vocal learning is the ability to modify vocal output on the basis of experience. Traditionally, species have been classified as either displaying or lacking this ability. A recent proposal, the vocal learning continuum, recognizes the need to have a more nuanced view of this phenotype and abandon the yes-no dichotomy. However, it also limits vocal learning to production of novel calls through imitation, moreover subserved by a forebrain-to-phonatory-muscles circuit. We discuss its limitations regarding the characterization of vocal learning across species and argue for a more permissive view.
| S-EPMC7145199 | biostudies-literature
Project description:This represents the first transcriptomic and epigenomic characterization of the vocalization-associated brain circuits of a non-human mammalian vocal learner (Egyptian fruit bat), yielding fundamental insights into the regulatory and molecular pathways underlying the evolution of complex vocal behavior in mammals.
2022-05-24 | GSE187366 | GEO
Project description:The Acoustic Complexity Index (ACI) is increasingly applied to the study of biodiversity in aquatic habitats. However, it remains unknown which types of acoustic information are highlighted by this index in underwater environments. This study explored the robustness of the ACI to fine variations in fish sound abundance (i.e. number of sounds) and sound diversity (i.e. number of sound types) in field recordings and controlled experiments. The ACI was found to be sensitive to variations in both sound abundance and sound diversity, making it difficult to discern between these variables. Furthermore, the ACI was strongly dependent on the settings used for its calculation (i.e. frequency and temporal resolution of the ACI algorithm, amplitude filter). Care should thus be taken when comparing ACI absolute values between studies, or between sites with site-specific characteristics (e.g. species diversity, fish vocal community composition). As the use of ecoacoustic indices presents a promising tool for the monitoring of vulnerable environments, methodological validations like those presented in this paper are of paramount importance in understanding which biologically important information can be gathered by applying acoustic indices to Passive Acoustic Monitoring data.
| S-EPMC6043554 | biostudies-literature
Project description:Determining the information content of animal vocalisations can give valuable insights into the potential functions of vocal signals. The source-filter theory of vocal production allows researchers to examine the information content of mammal vocalisations by linking variation in acoustic features with variation in relevant physical characteristics of the caller. Here I used a source-filter theory approach to classify female koala vocalisations into different call-types, and determine which acoustic features have the potential to convey important information about the caller to other conspecifics. A two-step cluster analysis classified female calls into bellows, snarls and tonal rejection calls. Additional results revealed that female koala vocalisations differed in their potential to provide information about a given caller's phenotype that may be of importance to receivers. Female snarls did not contain reliable acoustic cues to the caller's identity and age. In contrast, female bellows and tonal rejection calls were individually distinctive, and the tonal rejection calls of older female koalas had consistently lower mean, minimum and maximum fundamental frequency. In addition, female bellows were significantly shorter in duration and had higher fundamental frequency, formant frequencies, and formant frequency spacing than male bellows. These results indicate that female koala vocalisations have the potential to signal the caller's identity, age and sex. I go on to discuss the anatomical basis for these findings, and consider the possible functional relevance of signalling this type of information in the koala's natural habitat.
| S-EPMC4605621 | biostudies-literature