Find research datasets worth reusing

opencc-zeroJan 2024View details →

Reproductive state alters vocal characteristics of female North American red squirrels (Tamiasciurus hudsonicus)

Female advertisement of reproductive state and receptivity has the potential to play a large role in the mating systems of many taxa, but investigations of this phenomenon are underrepresented in the literature. North American red squirrels (Tamiasciurus hudsonicus) are highly territorial and engage in scramble competition mating, with males converging from spatially disparate territories to engage in mating chases. Given the narrow estrus window exhibited in this species, the ubiquitous use of vocalizations to advertise territory ownership, and the high synchronicity of males arriving from distant territories, we hypothesized that female vocalizations contain cues relating to their estrous state. To test this hypothesis, we examined the spectral and temporal properties of female territorial rattle vocalizations collected from females of known reproductive condition over 3 years. While we found no distinct changes associated with estrus specifically, we did identify significant changes in the spectral characteristics of rattles relating to both female body mass and reproductive state relative to parturition. To the best of our knowledge, this is the first evidence of changes in vocal characteristics associated with late pregnancy in a non-human mammal.

opencc-by-4.0May 2023View details →

AVID: Aalto Vocal Intensity Database

Data description: AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud, and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labeled sentence-wise using a total of 19 labels that support the utilisation of the data in ML-based studies of vocal intensity based on supervised learning. Further information can be found in the 'readme.docx' file from the upload. when collected the data: Data is collected in 2021 Citation: P. Alku, M. Kodali, L. Laaksonen, S.R. Kadiri, AVID: A speech database for machine learning studies on vocal intensity, Speech Communication, Vol. 157, Article 103039, 2024. <a href="https://doi.org/10.1016/j.specom.2024.103039" target="_blank" rel="noopener noreferrer">https://doi.org/10.1016/j.specom.2024.103039</a>

opencc-zeroFeb 2024View details →

Evidence for individual vocal recognition in a pair-bonding poison frog, Ranitomeya imitator

Individually distinctive vocalizations are widespread in nature, although the ability of receivers to discriminate these signals has only been explored through limited taxonomic and social lenses. Here, we asked whether anuran advertisement calls, typically studied for their role in territory defense and mate attraction, facilitate recognition and preferential association with partners in a pair-bonding poison frog (Ranitomeya imitator). Combining no- and two-stimulus choice playback experiments, we evaluated behavioral responses of females to male acoustic stimuli. Virgin females oriented to and approached speakers broadcasting male calls independent of caller identity, implying that females are generally attracted to male acoustic stimuli outside the context of a pair bond. When pair-bonded females were presented with calls of a mate and a stranger, they showed significant preference for calls of their mate. Moreover, behavioral responses varied with breeding status: females with eggs were faster to approach stimuli than females that were pair-bonded but did not currently have eggs. Our study suggests a potential role for individual vocal recognition in the formation and maintenance of pair bonds in a poison frog and raises new questions about how acoustic signals are perceived in the context of monogamy and biparental care.

Zebra finch dataset for the paper: Benchmarking nearest neighbor retrieval of zebra finch vocalizations across development

This is the dataset created in the paper "Benchmarking nearest neighbor retrieval of zebra finch vocalizations across development".

opencc-by-4.0Dec 2021View details →

evaluation data for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)"

Data contains sound files of mouse vocalization needed to reproduce the evaluation results for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)" If you use any of this data, cite the original source: <a href="https://doi.org/10.1016/j.anbehav.2020.09.006">https://doi.org/10.1016/j.anbehav.2020.09.006</a>

opencc-zeroApr 2022View details →

Differences in dogs' event related potentials in response to human and dog vocal stimuli: A non-invasive study

Recent advances in the field of canine neuro-cognition allow for the non-invasive research of brain mechanisms in family dogs. Considering the striking similarities between dog's and human (infant)'s socio-cognition at the behavioural level, both similarities and differences in neural background can be of particular relevance. The current study investigates brain responses of N=17 family dogs to human and conspecific emotional vocalisations using a fully non-invasive ERP paradigm. We found that similarly to humans, dogs show a differential ERP response depending on the species of the caller demonstrated by a more positive ERP response to human vocalisations compared to dog vocalisations in a time-window between 250-650 ms after stimulus onset. A later time-window between 800-900 ms also revealed a valence sensitive ERP response in interaction with the species of the caller. Our results are the first ERP evidence to show the species sensitivity of vocal neural processing in dogs along with indications of valence sensitive processes in later post-stimulus time-periods.

opencc-by-4.0Apr 2022View details →

Non-crop vegetation characteristics and vocalizing bird richness across 44 sites in Iowa, USA in June 2019

This data was derived from field work conducted in June 2019 where sixty AudioMoth passive acoustic monitors were placed along agricultural field margins in Iowa, USA. Twenty-five of the monitoring location were established by farmer and landowner collaborators, and the remaining (35) sites were established by the author (A.P.D.). Unique vocalizing bird species were counted in ninety-five recordings from 6 to 8 days during dawn hours per site. High resolution mapping identified non-crop vegetation and texture at spatial extents ranging from 100 to 1000 meters. Pesticide and fertilizer application were collected via a survey with collaborators. Site location names are included when the research site was an Iowa State Research and Demonstration Farm (ISRF). When the site was a collaborator, the site name was anonymized to "Collaborator" to respect the privacy of participants.

opencc-zeroApr 2024View details →

Data from: Crows 'count' the number of self-generated vocalizations

Producing a specific number of vocalizations with purpose requires a sophisticated combination of numerical abilities and vocal control. Whether any animal possesses such a capacity to voluntarily control the number of self-generated vocalizations is yet unknown. We demonstrate that crows can flexibly produce a variable number of one to four vocalizations in response to arbitrary cues associated with numerical values. The acoustic features of the first vocalization of a sequence were predictive of the total number of vocalizations, indicating a planning process. Moreover, the acoustic features of vocal units were foretelling of their order in the sequence and could be used to read-out counting errors during vocal production. Together, the crows' vocal enumeration capability could be an evolutionary precursor to symbolic counting found uniquely in humans.

opencc-zeroApr 2024View details →

Data from: Where's Whaledo: a software toolkit for array localization of animal vocalizations

Where's Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of Ziphius cavirostris. We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins.

opencc-zeroJun 2024View details →

Data from: A model of marmoset monkey vocal turn-taking

Vocal turn-taking has been described in a diversity of species. Yet a model that captures the various processes underlying this social behavior across species has not been developed. To this end, here we recorded a large and diverse dataset of marmoset monkey vocal behavior in social contexts comprising one, two and three callers and developed a model to determine the keystone factors that affect the dynamics of these natural communicative interactions. While a coupled oscillator model failed to account for turn-taking in marmosets, our model alternatively revealed four key factors that encapsulate much of patterns evident in the behavior, ranging from internal processes, such as the state of the individual, to social context driven suppression of calling. In addition, we show that the same key factors apply to the meerkat, a carnivorous species, in a multicaller setting. These findings indicate that vocal turn-taking is affected by a broader suite of mechanisms than previously considered and our model provides a predictive framework with which to further explicate this natural behavior and for direct comparisons with the analogous behavior in other species.

Figure 4 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 4. Seasonality in breeding records of White-rumped Monjita Xolmis velatus in Brazil based on citizen science data, the literature and this study.

Figure 3 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 3. Food delivered to nestlings of White-rumped Monjita Xolmis velatus, Rio Claro, São Paulo, Brazil. A: larva, B: Oligochaeta, C: Erythemis vesiculosa, D: Zammara tympanum, E: Lepidoptera (moth), F: Blattodea, G: Myriapoda, H: Kentropyx aff. paulensis (Luiz Carlos Ramassotti)

Figure 2 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 2. Food delivered to nestlings of White-rumped Monjita Xolmis velatus, Rio Claro, São Paulo, Brazil. A‒C = Scarabaeidae; D = Grylloidea; E = Ensifera; F = Tettigoniidae, Conocephalinae, Copiphorini; G‒H = Lycosidae (Luiz Carlos Ramassotti)

Figure 1 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 1. Nests of White-rumped Monjita Xolmis velatus. A: nest 1 with nestlings, Analândia, São Paulo, Brazil, 15 November 2008 (Rogério Carlos Machado); B: nest 2 with eggs, Marília, São Paulo, Brazil, 23 October 2010 (Manuel Gonzales); C‒F: nest 3, Rio Claro, São Paulo, Brazil; C: adult at entrance to PVC pipe, 20 October 2021 (Luiz Ramassotti); D: nest, 29 October 2021 (Carlos Otávio Araujo Gussoni); E: nest with nestlings, 20 October 2021 (Carlos Otávio Araujo Gussoni); F: nestling, 22 October 2021 (Carlos Otávio Araujo Gussoni)

Figure 5 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 5. Begging calls of a fledgling (A) and calls of an adult (B) White-rumped Monjita Xolmis velatus. Sonogram made using software Raven Pro 1.6.1 (Center for Conservation Bioacoustics 2019).

opencc-zeroJun 2024View details →

Data from: Vocalizations in the plains zebra (Equus quagga)

Acoustic signals are vital in animal communication, and quantifying these signals them is fundamental for understanding animal behaviour and ecology. Vocaliszations can be classified into acoustically and functionally or contextually distinct categories, but establishing these categories can be challenging. Newly developed methods, such as machine learning, can provide solutions for classification tasks. The plains zebra is known for its loud and specific vocaliszations, yet limited knowledge exists on the structure and information content of its vocaliszations. In this study, we employed both feature-based and spectrogram-based algorithms, incorporating supervised and unsupervised machine learning methods to enhance robustness in categoriszing zebra vocaliszation types. Additionally, we implemented a permuted discriminant function analysis (pDFA) to examine the individual identity information contained in the identified vocaliszation types. The findings revealed at least four distinct vocaliszation types he '"snort'," the '"soft snort'," the '"squeal'," and the '"quagga quagga'" with individual differences observed mostly in snorts, and to a lesser extent in squeals. Analyses based on acoustic features outperformed those based on spectrograms, but each excelled in characteriszing different vocaliszation types. We thus recommend the combined use of these two approaches. OuThisr study offers valuable insights into plains zebra vocaliszation, with implications for future comprehensive explorations in animal communication.

opencc-by-4.0Jul 2024View details →

Fin whale vocalizations recorded at OBS station BS080 in the northeast Pacific Ocean

Fin whale calls recorded by seismic stations in the northeast Pacific sped up 10 times to be audible to humans.

opencc-by-4.0Jul 2022View details →

Fig. 11 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 11 Rates of chatter calls in different magpie populations and individuals. a Each mark represents average chattering rate for a single bird from five populations indicated by colours. Figures are numbers for the outliers: 1, 2—jankowskii from the mixed population of Argun'; 3, 4, 5—hybrid birds from the hybridogeneous population of Kerulen. b Each mark represents average chattering rate for a series of chatterings of one selected individual representing jankowskii, leucoptera, and hybrid birds, respectively. Green mark—pair #6 jankowskii from Vladivostok; gray—pair #43 leucoptera from Tsasuchei, Transbaikalia; blue—pair #24 hybrids from Kerulen, eastern Mongolia. X-axis—number of elements per second in a total series of chattering; Y-axis— number of elements per second in a series of 5 elements of chattering

opencc-by-4.0Jul 2022View details →

Fig. 12 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 12 Violin plot diagram of the chatter call speed (elements per second) of Eurasian magpie populations across regions. X-axis presents a set of populations; Y-axis—elements per second. Box outlines the interquantile range (25%, 75%), whiskers represent range without outliers, central bar is the median, red dot is the mean, and figure shape is the probability density. The brackets on the top denote statistically significant pairwise differences (GamesHowell test, p<0.05)