Vocal Patterns Research Articles

Effects of Hydration and a Hyaluronic Acid-Containing Lozenge on Voice Parameters in Conjunction With a Vocal Loading Test.

This study explores the effects of water intake and a hyaluronic acid (HA)-containing lozenge on acoustic measurements and vocal oscillation patterns investigated after a vocal loading test (VLT). Ten healthy subjects (five females, five males) read out loud a standardized text for 10 min at a target level of 80 dB(A), measured 30 cm from the mouth, under three conditions but each after fasting for 2 hr: (a) drinking 0.7 l of water, (b) sucking an HA-containing lozenge, and (c) neither of both before the VLT. The dysphonia severity index (DSI) was assessed before and after the reading task. Additionally, high-speed videolaryngoscopy (HSV), electroglottography, and an audio signal during sustained phonation on the vowel /i/ before and after the VLT were analyzed. The glottal area waveform was derived from the HSV footage. DSI values decreased for the H2O and HA group, but reached statistical significance only for the H2O condition, while remaining stable for the control condition. These DSI decreases were driven by increases in minimum sound pressure level intensity (Imin)-again with statistical significance solely for the water intake intervention. Statistically nonsignificant changes were observed regarding periodicity and perturbation parameters across all conditions. No phase differences or aperiodicities were apparent in the phonovibrograms. Hydration and an HA lozenge did not significantly alter vocal fold biomechanics after a VLT. However, the decrease in DSI values with increased Imin suggests a reduced vocal capacity for the H2O condition. https://doi.org/10.23641/asha.28271285.

The Effect of Subglottic Stenosis Severity on Vocal Fold Vibration and Voice Production in Realistic Laryngeal and Airway Geometries Using Fluid–Structure–Acoustics Interaction Simulation

This study investigates the impact of subglottic stenosis (SGS) on voice production using a subject-specific laryngeal and airway model. Direct numerical simulations of fluid–structure–acoustic interaction were employed to analyze glottal flow dynamics, vocal fold vibration, and acoustics under realistic conditions. The model accurately captured key physiological parameters, including the glottal flow rate, vocal fold vibration patterns, and the first four formant frequencies. Simulations of varying SGS severity revealed that up to 75% stenosis, vocal function remains largely unaffected. However, at 90% severity, significant changes in glottal flow and acoustics were observed, with vocal fold vibration remaining stable. At 96%, severe reductions in glottal flow and acoustics, along with marked changes in vocal fold dynamics, were detected. Flow resistance, the ratio of glottal to stenosis area, and pressure drop across the vocal folds were identified as critical factors influencing these changes. The use of anatomically realistic airway and vocal fold geometries revealed that while anatomical variations minimally affect voice production at lower stenosis grades, they become critical at severe stenosis levels (>90%), particularly in capturing distinct anterior–posterior opening patterns and focused jet effects that alter glottal dynamics. These findings suggest that while simplified models suffice for analyzing mild to moderate stenosis, patient-specific geometric details are essential for accurate prediction of vocal fold dynamics in severe cases.

Clustering analysis of Yue opera character tone trends based on quantum particle swarm optimization for fuzzy C-means.

This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. The second-order difference method extracts tonal trend features. We introduce a fuzzy C-means clustering method enhanced by quantum particle swarm optimization (QPSO) to manage data uncertainties, improving classification accuracy and convergence speed. Additionally, we employ a cross-correlation function to eliminate uncertainties from tonal transition redundancies. We designed a detection algorithm using trend data to validate our clustering method, thereby enhancing the accuracy of the analysis of tonal ranges and potential models. This method detects whether Yue Opera adheres to traditional rhythmic norms and models the regularity of musical tones and vocal patterns. Simulation results reveal that our approach achieves a 91.4% accuracy in classifying vocal styles, surpassing traditional methods and demonstrating its potential for identifying various styles. This research offers technical support for Yue Opera music education and interdisciplinary research. The findings enhance the quality of artistic creation and performance in Yue Opera, ensuring its preservation and development.

Neural sensitivity to frequency changes in song structure in a high-order auditory area reflects tutor song memory in adult songbirds.

Vocal learners, including humans and songbirds, acquire their complex vocalizations by accurately memorizing and imitating the vocal patterns of other individuals. In songbirds, the caudomedial nidopallium (NCM), considered the secondary auditory region, has been suggested to play a critical role in memorizing and recognizing the songs of tutors. However, the mechanisms by which NCM neurons encode the acoustic information of tutor song are not yet fully understood. Here, we investigate the neural representation of tutor song information in NCM neurons by examining their sensitivity to spectral changes in song structure, using electrophysiological recordings in anesthetized male zebra finches. We manipulated the acoustic structures of both tutor songs and unfamiliar conspecific songs by shifting the fundamental frequency (FF) of harmonic syllables by various frequency steps and recorded neural responses to those FF-shifted and original songs. Our results demonstrate that NCM neurons are highly sensitive to FF shifts in tutor song but much less in unfamiliar conspecific song, providing novel evidence for neural encoding of tutor song information in NCM neurons. Moreover, we find that the effects of FF shifts on neural responses depend on the direction of FF shifts. These findings suggest that NCM neurons encode detailed information of tutor song, which can serve as a tutor song template required for song learning.

Cingulate cortex shapes early postnatal development of social vocalizations.

The social dynamics of vocal behavior has major implications for social development in humans. We asked whether early life damage to the anterior cingulate cortex (ACC), which is closely associated with socioemotional regulation more broadly, impacts the normal development of vocal expression. The common marmoset provides a unique opportunity to study the developmental trajectory of vocal behavior, and to track the consequences of early brain damage on aspects of social vocalizations. We created ACC lesions in neonatal marmosets and compared their pattern of vocalization to that of age-matched controls throughout the first 6 weeks of life. We found that while early life ACC lesions had little influence on the production of vocal calls, developmental changes to the quality of social contact calls and their associated sequential and acoustic characteristics were compromised. These animals made fewer social contact calls, and when they did, they were short, loud and monotonic. We further determined that damage to ACC in infancy results in a permanent alteration in downstream brain areas known to be involved in social vocalizations, such as the amygdala and periaqueductal gray. Namely, in the adult, these structures exhibited diminished GABA-immunoreactivity relative to control animals, likely reflecting disruption of the normal inhibitory balance following ACC deafferentation. Together, these data indicate that the normal development of social vocal behavior depends on the ACC and its interaction with other areas in the vocal network during early life.

Using Machine Learning to Explore Multimodal Digital Markers for Early Detection of Cognitive Impairment in Alzheimer’s Disease

AbstractBackgroundRecent technological advancements have revolutionized our approach to healthcare, enabling us to harness the potential of smartphones and wearables to collect data that can be used to characterize Alzheimer’s disease (AD) heterogeneity and to develop digital biomarkers. Our focus is to create comprehensive cross‐domain digital datasets and establish an infrastructure that allows for seamless data sharing. Central to accelerating the potential of digital biomarkers for more accurate and early detection is privacy‐protecting data access, which when combined with deep molecular phenotyping, will enhance our understanding of the biological mechanisms underlying clinical expression.MethodsIn the preliminary phase of this project, we analyzed data from 64 participants from the Boston University Alzheimer’s Disease Research Center and encompassing approximately 1480 variables. Our analysis approach leverages a novel machine learning (ML) technology, Attractor AI, that is capable of differentiating causal and non‐causal subpopulations within small patient or study populations and large volumes of measures, enhancing the efficacy of predictive models.ResultsWe were able to subcategorize 50% of the 27 cognitively impaired (CI) subjects. A notable discovery was a distinct subpopulation of 8 individuals, 7 of whom were CI, characterized significantly by higher sleep‐derived variables such as various desaturation thresholds and periodicity measures (p=0.008‐0.00007). Additionally, incorporating maximum heart rate, revealed another group of 8 subjects, 6 identified as CI, distinguished by elevated heart rate during one or more of their measuring instances (p=10‐10).ConclusionsWhile these results are preliminary, they signal a promising direction to cluster subgroups of people along similar dimensions, laying the groundwork for a precision medicine solution. Our future endeavors include expanding the scope of multimodal digital data to encompass aspects like vocalization and speech patterns derived from cognitive assessments, gait analysis, physical activity, and other cognitive tests. The integration of these diverse data streams, coupled with our preliminary sleep analysis findings, has the potential to result in a robust and accurate subgrouping system for CI to help identification of AD risk pathways that might be amenable to early intervention and either delay or prevent potential transition to AD.

Deciphering avian emotions: A novel AI and machine learning approach to understanding chicken vocalizations

In this groundbreaking study, we present a novel approach to interspecies communication, focusing on the understanding of chicken vocalizations. Leveraging advanced mathematical models in artificial intelligence (AI) and machine learning, we have developed a system capable of interpreting various emotional states in chickens, including hunger, fear, anger, contentment, excitement, and distress. Our methodology employs a cutting-edge AI technique we call Deep Emotional Analysis Learning (DEAL), a highly mathematical and innovative approach that allows for the nuanced understanding of emotional states through auditory data. DEAL is rooted in complex mathematical algorithms, enabling the system to learn and adapt to new vocal patterns over time. We conducted our study with a sample of 80 chickens, meticulously recording and analyzing their vocalizations under various conditions. To ensure the accuracy of our system’s interpretations, we collaborated with a team of eight animal psychologists and veterinary surgeons, who provided expert insights into the emotional states of the chickens. Our system demonstrated an impressive accuracy rate of close to 80%, marking a significant advancement in the field of animal communication. This research not only opens up new avenues for understanding and improving animal welfare but also sets a precedent for further studies in AI-driven interspecies communication. The novelty of our approach lies in its application of sophisticated AI techniques to a largely unexplored area of study. By bridging the gap between human and animal communication, we believe our research will pave the way for more empathetic and effective interactions with the animal kingdom.

A quick response, but not too much: Experienced mangabeys (Cercocebus torquatus) lose interest in contact call exchanges that do not respect this "conversational rule".

Several non-human primate species engage in vocal exchanges of contact calls, throughout the day in peaceful contexts. These vocal exchanges have been compared to human conversations because vocalizations are uttered in turn-taking: a temporal pattern where interlocutors minimize silences and avoid overlaps. But observing such a pattern in the spontaneous production of a species, as is the case with red-capped mangabeys (Cercocebus torquatus), is not enough to make it a rule. Another prerequisite is that the pattern is expected by the animal. Here, we conducted a playback experiment using the violation-of-expectation paradigm to test whether captive red-capped mangabeys react differently to usual vs unusual interactive temporal patterns. We played back vocal exchanges with usual minimized response time (0.5 sec), with unusual longer response time (1.5 sec) and with unusual call overlap to 12 adult captive male mangabeys. For each individual, we measured the occurrences and durations of head orientation toward the loudspeaker after the stimuli. The interest of individuals varied according to the vocal exchange temporal pattern in interaction with their age. Indeed, the older (and thus more socially experienced) an individual was, the less interested he became after an unusual vocal exchange, i.e. a vocal exchange with call overlap or with a delayed response time. These findings suggest that experience shapes attention towards more socially relevant situations, and thus that turn-taking can be qualified as a social rule.

The impact of touristic whale-watching on Delphinus Delphis and Tursiops truncatus in the Algarve Coast: Combining acoustic analysis and land observations

Touristic whale watching (WW) is an important socioeconomic activity worldwide. Recently, short and long-term impacts caused by WW have been reported for several cetacean species, including Delphinus delphis and Tursiops truncatus, the two most observed species in Portugal. Most of the operations in mainland Portugal are concentrated on the South Coast (Algarve). However, despite their importance for the region, studies focused on the impact of this activity on the animals are still scarce. We used acoustic behaviour analysis and land-based observations to determine the alterations in the vocalization patterns of these animals caused by WW vessels and the length of exposure to this stressor. We found WW presence significantly altered five out of six acoustic parameters of whistles, whistle rate, and whistle type distribution for D. delphis, whereas T. truncatus exhibited significant changes in three acoustic parameters. Conversely, T. truncatus significantly reduced echolocation click rates, while D. delphis did not show significant changes. We also found that the Central region of this coast is the most affected, with animals exposed to WW vessels for up to 38.9% of daylight hours. Furthermore, 66.7% of the observation time in this area had several vessels that exceeded the limit established by law. These results suggest that our study species are impacted by WW, while D. delphis is more affected during social behaviours, and T. truncatus is more affected during foraging activities. Both behaviours are biologically significant, and their disturbance might, in the long term, lead to adverse effects at the population level by decreasing reproductive success and fitness. A gap in knowledge, inadequate legislation and compliance issues threaten the development of a sustainable WW industry in the Algarve and might endanger local populations, therefore immediate attention is needed.

Acoustic features of vocalizations in typically developing and autistic infants in the first year

We describe acoustic patterns across the five most prominent vocal types in typically developing infants (TD) and compare them with patterns in infants who develop autism (ASD) or a developmental disability (DD) not related to autism. Infant-directed speech (IDS) is a potentially important influence on such vocal acoustic patterns. Both acoustic patterns and effects of IDS are important for understanding the earliest origins of communication disorders. To compare duration, pitch and loudness of infant vocalizations for three groups of infants (TD, ASD, DD) in circumstances with high or low amounts of IDS. Two five-minute segments from each of 1259 all-day recordings across the first year were coded and acoustically analyzed for three groups of infants (130 TD, 44 ASD, 21 DD). Duration, mean fundamental frequency, and root mean square amplitude were determined for >162,000 infant utterances. Cries were longest and loudest, and vowel-like sounds were shortest of the five vocal types in all groups. TD infants showed significant alterations in vocal acoustics during periods of high IDS. Strong similarities in acoustic patterns occurred across the three groups, but only the TD group showed significant acoustic effects of IDS.

Social factors affect vocal activity patterns of two common call types in Little Owl males

Social factors affect vocal activity patterns of two common call types in Little Owl males

Broiler chicken vocalization analysis during a medium-scale heat stress experiment

The poultry industry in Flanders, Belgium, is characterized by highly efficient production mechanisms. To improve awareness for animal health and welfare, the use of neural network-based broiler vocalization detectors has been recently proposed in scientific literature. However, these tools have not yet been tested when used continuously in large-scale deployments. This paper describes the deployment of the broiler vocalization detector to analyze the sonic environment during four experimental rounds, each containing 560 broilers, in a medium scale housing at ILVO, Belgium. Half the broilers were subjected to periods of heat stress (ambient temperature increased to 32°C for 6h/day for 10 days during the last weeks of their lives) and the effect on the diurnal broiler vocalization pattern and occurrence of warbles, pleasure notes, short beeps and distress calls is discussed. While these four different types of vocalizations are common vocalizations in young birds, new types of vocalizations are emerging at an older age. A clustering has been performed to further analyze these new types of vocalizations occurring.

Early Caregiver Predictability Shapes Neural Indices of Statistical Learning Later in Infancy.

Caregivers play an outsized role in shaping early life experiences and development, but we often lack mechanistic insight into how exactly caregiver behavior scaffolds the neurodevelopment of specific learning processes. Here, we capitalized on the fact that caregivers differ in how predictable their behavior is to ask if infants' early environmental input shapes their brains' later ability to learn about predictable information. As part of an ongoing longitudinal study in South Africa, we recorded naturalistic, dyadic interactions between 103 (46 females and 57 males) infants and their primary caregivers at 3-6 months of age, from which we calculated the predictability of caregivers' behavior, following caregiver vocalization and overall. When the same infants were 6-12-months-old they participated in an auditory statistical learning task during EEG. We found evidence of learning-related change in infants' neural responses to predictable information during the statistical learning task. The magnitude of statistical learning-related change in infants' EEG responses was associated with the predictability of their caregiver's vocalizations several months earlier, such that infants with more predictable caregiver vocalization patterns showed more evidence of statistical learning later in the first year of life. These results suggest that early experiences with caregiver predictability influence learning, providing support for the hypothesis that the neurodevelopment of core learning and memory systems is closely tied to infants' experiences during key developmental windows.

Living Together, Singing Together: Revealing Similar Patterns of Vocal Activity in Two Tropical Songbirds Applying BirdNET.

In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM is the generation of a significant amount of data, which often requires the use of machine learning tools for automated recognition. Here, we couple PAM with BirdNET, a free-to-use sound algorithm to assess, for the first time, the precision of BirdNET in predicting three tropical songbirds and to describe their patterns of vocal activity over a year in the Brazilian Pantanal. The precision of the BirdNET method was high for all three species (ranging from 72 to 84%). We were able to describe the vocal activity patterns of two of the species, the Buff-breasted Wren (Cantorchilus leucotis) and Thrush-like Wren (Campylorhynchus turdinus). Both species presented very similar vocal activity patterns during the day, with a maximum around sunrise, and throughout the year, with peak vocal activity occurring between April and June, when food availability for insectivorous species may be high. Further research should improve our knowledge regarding the ability of coupling PAM with BirdNET for monitoring a wider range of tropical species.

Seasonal variation in the ultrasonic vocal activity of Humboldt's flying squirrel (Glaucomys oregonensis).

Most mammals rely on vocal communication to increase survival and reproductive success. While the functions of audible vocalizations have been well-studied across mammal species, ultrasonic vocalizations in small mammals outside of bats are less understood. North American flying squirrel species (Glaucomys spp.), including the newly described Humboldt's flying squirrel (Glaucomys oregonensis), produce numerous call-types that extend into the ultrasonic range. To investigate the potential function of ultrasonic call-types in the Humboldt's flying squirrels, we used ultrasonic recorders to record squirrels in the wild across multiple seasons to determine if there are temporal and seasonal patterns in nightly vocal activity and rates of different call-types. We recorded Humboldt's flying squirrels in two geographic locations - Humboldt and San Bernardino counties-in California from 2018 to 2022 in the summer and winter across multiple study areas. We found that although seasonal weather conditions differ between locations, flying squirrels in Humboldt and San Bernardino had similar vocal activity patterns across nightly active periods between locations and between summer and winter. Nightly activity patterns of when the three main chirp-like call-types (arc chirps, tonal chirps, upsweeps) were given varied between seasons in both geographic locations, and these call-types were given at greater rates in the summer in San Bernardino, but rates did not vary by season in Humboldt. Trills, the most structurally complex of the four main call-types, were produced more in the summer than in winter, and also differed in their nightly activity patterns, in both geographic locations. Flying squirrels may use certain call-types earlier or later in the nightly active period due to their potentially varying functions, and may produce more trills in the summer coinciding with the breeding season. Further understanding of the function of different call-types can provide insight into social, foraging, and antipredator behavior of this nocturnal and elusive species.

Singing in the rain! Climate constraints on the occurrence of indri's song.

The study of how animals adapt their behaviors depending on weather variables has gained particular significance in the context of climate change. This exploration offers insights into endangered species' potential threats and provides information on the direction to take in conservation activities. In this context, noninvasive, cost-effective, and potentially long-term monitoring systems, such as Passive Acoustic Monitoring (PAM), become particularly appropriate. Our study investigates the relationship between weather variables and the vocal behavior of Indri indri, the sole singing lemur species, within Madagascar's Maromizaha New Protected Area. Using PAM, we explore the factors shaping the vocalization patterns of this primate species in response to some environmental factors in their natural habitat. Analysis of an extensive audio data set collected across different years revealed the differential influence of temperature and precipitation on Indri indri vocal activity. We found that rainfall negatively influenced the emission of the vocalizations while warmer temperatures correlated with a greater emission of songs. The various environmental factors we considered also affected the timing of vocal emissions, showing the same pattern. Furthermore, our study confirms, once again, the strength of PAM as a valuable tool for studying vocal animal communication quickly, giving us information about long-term behavioral patterns that would be difficult to get in other ways. This research gives us further valuable information about how indris use vocalizations in their environment and how they adjust to environmental changes.

Recognition of domestic <em>Felis Catus</em> vocalisations using a computational associative model

The cat (Felis catus) lives around or with humans and is considered one of the most popular pets in the world. Domestic cats have an extensive and complex vocalisation repertoire, and little is known about the meaning of their vocalisations; these animal sounds have been studied to determine their behavioural characteristics. This work implemented a computational model for cat vocalisation as a signal process to characterise it. Additionally, a recognition model is proposed to identify and associate domestic cat vocalisations with cat intentions. Domestic cat vocalisations were studied by obtaining sounds from audio and video sources stored in the web cloud and processing signals (distress, hiss, howl, meow, fright, purr, sexual call, and trill vocalisations) that belong to some cat situations (soliciting attention, complaining, surprise, threatening, welcoming, fighting, feeling hungry, mating, etc.). In measured signals, environmental noise was removed using a computational algorithm to increase the effectiveness of the recognition process. As a first approach, cat vocalisation patterns were extracted using Welch's method for spectral density estimation, appreciating vocalisation frequency characteristics and statistics. Additionally, a similarity function to compare cat vocalisation signals was developed, excluding useless patterns that could introduce computational problems in the learning phase of the recognition model. Then, an associative memory, the Lernmatrix Thresholded model, was used to recognise the domestic cat vocalisation needed, and pair associations between sounds and moods related to the recognition process result were established, i.e., identifying cat intention through vocalisation by analysing the signals emitted. The experimental results show the performance of the thresholded Lernmatrix among different pattern recognition models, where accurate vocalisation recognition rates are obtained. This computational model emerges as a suitable tool that could be helpful in animal care and ethology science to characterise the behaviour of animals through their vocalisation.

Acoustic sensors to detect the rate of cow vocalization in a complex farm environment

Acoustic technologies provide a method to investigate cow vocalization. This study demonstrated that collar attached acoustic sensors can determine vocalization from continuous acoustic recordings of cows under grazing conditions. In this study a total of 4843 vocalizations from 10 cows were observed over 3 days. Algorithms were developed to differentiate cow vocalization from other farm noises and the F1-statistic for the vocalization classification model was 95 % for the test dataset. Models for the rate of vocalization per cow (R2 = 0.99), average vocalization duration per cow (R2 = 0.83), and the coefficient of variation (CV) of duration of vocalization per cow (R2 = 0.73) in the validation dataset demonstrate that it is feasible to determine vocalization traits that provide information on the state of the animal. There was also significant between-cow variation in the vocalization traits examined in this study, with a rate of vocalization per cow that ranged from 20 to 584 events day−1, an average duration of vocalization per cow that ranged from 1.0 to 1.4 s, a CV of duration of vocalization per cow that ranged from 0.28 to 0.42. The transition probabilities between vocalization type (closed, mixed, open mouth) were also calculated for each cow and there was a 1.3–14-fold between-cow variation in the transition probabilities. The total number of vocalizations per hour interval was highly variable and ranged from zero to 250 vocalizations, depending on cow and time of day. The cows largely vocalized at similar times of the day, although we demonstrated that the technology could identify cows that differ in their pattern of vocalization relative to the herd and relative to their vocalization patterns on previous days. These models can be used to investigate the role of management and environmental factors on cow vocalization, and to develop technologies that respond to cow vocalization behaviour.

Clemastine improves emotional and social deficits in adolescent social isolation mice by reversing demyelination

Adolescence is a critical period for social experience-dependent oligodendrocyte maturation and myelination. Adolescent stress predisposes to cause irreversible changes in brain structure and function with lasting effects on adulthood or beyond. However, the molecular mechanisms linking adolescent social isolation stress with emotional and social competence remain largely unknown. In our study, we found that social isolation during adolescence leads to anxiety-like behaviors, depression-like behaviors, impaired social memory and altered patterns of social ultrasonic vocalizations in mice. In addition, adolescent social isolation stress induces demyelination in the prefrontal cortex and hippocampus of mice, with decreased myelin-related gene expression and disrupted myelin structure. More importantly, clemastine was sufficient to rescue the impairment of emotional and social memory by promoting remyelination. These findings reveal the demyelination mechanism of emotional and social deficits caused by social isolation stress in adolescence, and provides potential therapeutic targets for treating stress-related mental disorders.

경량화 MobileNet을 활용한 축산 데이터 음성 분석

Pigs express their reactions to their environment and health status through a variety of sounds, such as grunting, coughing, and screaming. Given the significance of pig vocalizations, their study has recently become a vital source of data for livestock industry workers. To facilitate this, we propose a lightweight deep learning model based on MobileNet that analyzes pig vocal patterns to distinguish pig voices from farm noise and differentiate between vocal sounds and coughing. This model was able to accurately identify pig vocalizations amidst a variety of background noises and cough sounds within the pigsty. Test results demonstrated that this model achieved a high accuracy of 98.2%. Based on these results, future research is expected to address issues such as analyzing pig emotions and identifying stress levels.