Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
693
datasets available to search
ShareScore release 0.9.0
Dataset results
693 results for “Vocalization”
Figure 2. M1 in Acoustic analysis of vocalization and the behavioral response associated to sound production of the nine banded armadillo Dasypus novemcinctus (Mammalia, Cingulata, Dasypodidae)
Figure 2. M1 (marked with white tape at the middle of its moveable bands) Behavior (A) when cornered after being submitted to the presence of other male subject, (B) at a second moment, when the other animal approaches from its back, and then (C) M1 bends its body left to prevent the contact, with the other male scratching the basis of its tail. In (D) the other male bipedally projects its belly against M1's back, and the latter finally changes its position.
Figure 2 in First record of Scinax centralis (Anura, Hylidae) in the Triângulo Mineiro region, state of Minas Gerais, southeastern Brazil, with further data on its vocalization
Figure 2. Map of distribution of Scinax centralis. (1) Floresta Nacional de Silvânia, Silvânia; (2) Mesquita stream, Brasília; (3) Córrego do Fogo, Orizona; (4) Ipameri; (5) Mariquita stream, Campo Alegre de Goiás; (6) Fazenda Limoeiro, Cumari; (7) District of Amanhece, Araguari. Abbreviations for Brazilian states: DF = Distrito Federal, GO = Goiás, MG = Minas Gerais. Literature records from Moura et al. (2010). Region in faded pink represents the Atlantic Forest biome.
Figure 1 in First record of Scinax centralis (Anura, Hylidae) in the Triângulo Mineiro region, state of Minas Gerais, southeastern Brazil, with further data on its vocalization
Figure 1. Spectrogram and its respective oscillogram of: (A) a type A (= advertisement) call of a male of Scinax centralis from Araguari, Triangulo Mineiro region, state of Minas Gerais, southeastern Brazil (sound file = Scinax_centralAraguariMG6bCBS_AAGmt; inset: a male individual (AAG-UFU 1822: 21.6 mm SVL)); and (B) a click-like note followed by a long squawk-like note (same sound file as in A). See further recording details in Appendix 2. Relative amplitudes in spectrogram figures have a grey scale in which black is the maximum amplitude (0 dB).
Depleted cultural richness of an avian vocal mimic in fragmented habitat
<p>Aim: Conservation has recently shifted to include behavioural or cultural diversity, adding substantial value to conservation efforts. Habitat loss and fragmentation can deplete diversity in learnt behaviours such as bird song by reducing the availability of song tutors, yet these impacts are poorly understood. Vocal mimicry may be particularly sensitive to habitat loss and fragmentation through the resulting reduction in both heterospecific models and conspecific tutors. Here we examine the relationship between habitat availability and mimetic repertoire size and composition in male Albert's lyrebirds (<em>Menura alberti</em>), a near-threatened species renowned for its remarkable mimetic abilities.</p> <p>Location: Eastern Australia</p> <p>Methods: We calculated repertoire size and composition from recordings of male Albert's lyrebirds from throughout the species' range. We estimated patch size and local habitat availability using a species distribution model and remotely sensed vegetation types. We assessed the local model species assemblage through species distribution models and automated acoustic detectors.</p> <p>Results: Individual males in smaller habitat patches, or in areas with a lower proportion of suitable habitat, mimicked fewer model species and fewer vocalisation types, though they mimicked comparatively more vocalisations from each model species than individuals in larger patches or with more intact habitat. All model species were likely to occur in most study sites, suggesting that repertoires are not driven by the availability of model species.</p> <p>Main conclusions: Our results suggest that mimetic repertoire sizes are influenced by habitat availability through the number of lyrebird tutors. Further, individuals in disturbed habitat may partially compensate for mimicking fewer species by mimicking more vocalisations from each species. This study supports the hypothesis that cultural diversity may be impoverished by habitat loss and fragmentation in a similar way to genetic diversity. Variation in song diversity may therefore signal population health and highlight populations in particular need of conservation action.</p>
Replication data for "Gekko gecko as a model organism for understanding aspects of laryngeal vocal evolution"
<p>This dataset contains raw data and analysis code used in the preparation of the manuscript “<em>Gekko gecko</em> as a model organism for understanding aspects of laryngeal vocal evolution”.</p>
Neural FoxP2 expression in an aging open-ended vocal learner
<p>Most vocal learning species exhibit an early critical period during which their vocal control neural circuity can facilitate the acquisition of new vocalizations. Some taxa, most notably humans and parrots, retain this neurobehavioral plasticity throughout adulthood. Downregulation of the transcription factor FoxP2 in both songbird and parrot vocal control nuclei has been identified as a key expression pattern facilitating vocal learning. We hypothesize that open-ended vocal learning is vulnerable to cognitive decline, and that this deterioration will be reflected in age-related changes in neural FoxP2 expression. We tested this hypothesis in the budgerigar (<em>Melopsittacus undulatus</em>), a small gregarious parrot in which adults converge on shared call types in response to shifts in group membership. We formed novel flocks of 4 previously unfamiliar males belonging to the same age class, either "young adult" (6 mo-1 yr) or "older adult" (≥ 3 yr), and then collected audio-recordings over a 20-day learning period to assess vocal learning ability. Following behavioral recording, whole brains were extracted and immunohistochemistry was performed to measure FoxP2 protein expression in a parrot vocal learning center, the magnocellular nucleus of the medial striatum (MMSt), and its adjacent striatum. We find similarly downregulated FoxP2 expression and equivalent vocal plasticity and vocal convergence in young and older adults suggesting the maintenance of two components of open-ended learning into old age in the budgerigar. No relationship between individual variation in vocal learning measures and FoxP2 expression was detected.</p>
Fig. 1 in Preliminary data on the defensive behavior and vocalization of the Lesser blind mole rat, Nannospalax leucodon (Nordmann, 1840)
Fig. 1. Defensive posture of the Lesser mole rat, Nannospalax leucodon (Nordmann, 1840).
Fig 3 in Preliminary data on the defensive behavior and vocalization of the Lesser blind mole rat, Nannospalax leucodon (Nordmann, 1840)
Fig 3. Consecutive series of 10 harsh calls.
Fig 2 in Preliminary data on the defensive behavior and vocalization of the Lesser blind mole rat, Nannospalax leucodon (Nordmann, 1840)
Fig 2. Harsh calls consisting of sequences of single very short phases.
Fig. 1 in Case of alarm vocalization in a colony of Microtus guentheri (Danford & Alston, 1880) (Mammalia, Rodentia, Arvicolidae) from Southern Bulgaria
Fig. 1. Spectrogram of the alarm whilst of Guenter's vole Microtus guentheri.
Turkish şarkı vocal dataset
<p>Turkish şarkı vocal dataset is a collection of recordings of compositions from the vocal form şarkı. The recordings are selected from a musicBrainz collection of Turkish music<a href="http://musicbrainz.org/collection/544f7aec-dba6-440c-943f-103cf344efbb"> http://musicbrainz.org/collection/544f7aec-dba6-440c-943f-103cf344efbb </a></p> <p>The collection has annotations of lyrics. Each lyrical phrase is aligned to its corresponding segment in the audio. </p> <p><strong>THE DATASET </strong></p> <p><strong>Audio music content</strong></p> <p><strong>version 2</strong></p> <p>It is a modification with some added and some omitted recordings of Version 1 and features 12 performances of 11 different compositions. 8 sung by female and 4 by male.</p> <p><strong>Lyrical phrases annotations</strong></p> <p>The audio is segmented into one-section chunks (a section is nakarat, meyan etc.)</p> <p>Each audio segment is aligned to the lyrical phrases. A phrase corresponds roughly to a musical bar and contains 1 or 2 words. </p> <p>An annotation file is in .TextGrid format of Praat.</p> <p><strong>Using this dataset </strong></p> <p>Please cite the following publication for if you use the dataset in your work:</p> <blockquote> <p><a href="http://www.mtg.upf.edu/biblio/author/810">Dzhambazov, G.</a>, & <a href="http://www.mtg.upf.edu/biblio/author/893">Serra X.</a> (2015). <a href="http://www.mtg.upf.edu/node/3266">Modeling of Phoneme Durations for Alignment between Polyphonic Audio and Lyrics</a>. Sound and Music Computing Conference 2015. </p> </blockquote> <p><a href="http://hdl.handle.net/10230/27614">http://hdl.handle.net/10230/27614</a></p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p><strong>CONTACT</strong></p> <p>If you have any questions or comments about the dataset, please feel free to write to us.</p> <p>Georgi Dzhambazov<br> Music Technology Group,<br> Universitat Pompeu Fabra,<br> Barcelona, Spain<br> georgi <dot> dzhambazov <at> upf <dot>edu</p> <p><strong>RELASE LINK</strong></p> <p><a href="https://github.com/georgid/turkish-makam-lyrics-2-audio-test-data-synthesis/releases/tag/2.0">https://github.com/georgid/turkish-makam-lyrics-2-audio-test-data-synthesis/releases/tag/2.0</a></p> <p> </p> <p><a href="http://compmusic.upf.edu/turkish-sarki">http://compmusic.upf.edu/turkish-sarki</a></p>
Vocal Imitation Set v1.1.3 : Thousands of vocal imitations of hundreds of sounds from the AudioSet ontology
<p>The VocalImitationSet is a collection of crowd-sourced vocal imitations of a large set of diverse sounds collected from Freesound (<a href="https://freesound.org/">https://freesound.org/</a>), which were curated based on Google's AudioSet ontology (<a href="https://research.google.com/audioset/">https://research.google.com/audioset/</a>). We expect that this dataset will help research communities obtain a better understanding of human's vocal imitation and build a machine understand the imitations as humans do.</p> <p>See <a href="https://github.com/interactiveaudiolab/VocalImitationSet">https://github.com/interactiveaudiolab/VocalImitationSet</a> for more information about this dataset and its latest updates.</p> <p>For citations, please use this reference:</p> <p>Bongjun Kim, Madhav Ghei, Bryan Pardo, and Zhiyao Duan, "Vocal Imitation Set: a dataset of vocally imitated sound events using the AudioSet ontology," <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)</em>, Nov. 2018.</p> <p>Contact Info:</p> <p>- Interactive Audio Lab: <a href="http://music.eecs.northwestern.edu/">http://music.eecs.northwestern.edu</a></p> <p>- Bongjun Kim <a href="mailto:bongjun@u.northwestern.edu">bongjun@u.northwestern.edu</a> | <a href="http://www.bongjunkim.com/">http://www.bongjunkim.com</a></p> <p>- Bryan Pardo <a href="mailto:pardo@northwestern.edu">pardo@northwestern.edu</a> | <a href="http://www.bryanpardo.com/">http://www.bryanpardo.com</a></p>
Audio from: 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model'
<p>Audio files associated with the paper 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model', presented at the International Congress of Phonetic Sciences 2019, Melbourne, Australia.</p>
Deep convolutional neural network for owl vocal identification
<p>This repository contains all the code and data necessary to replicate the results presented in Ruff et al. 2019, "Automated identification of avian vocalizations with deep convolutional neural networks", and is published in support of that manuscript. The folder includes several Python scripts, our trained convolutional neural network (CNN), and a set of 164,210 spectrogram images that were reviewed to generate CNN performance metrics. We include the CNN's predicted class scores for the test images as well as the set of labels assigned to the same images by experienced human technicians. The published article can be found here: <a href="https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.125">https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.125</a></p> <p>As presented, the CNN is designed to accept grayscale PNG images at 500x129 resolution and will generate a set of seven class scores for each image. Class scores are the softmax activation from the final (seven unit) fully-connected layer of the CNN. Scores are bounded between 0 and 1 and sum to 1 for each image. This means target classes are implicitly treated as mutually exclusive (i.e., each image belongs to exactly one class), although in reality some images contain calls from >1 target species.</p> <p>The different scripts and their functions are as follows:<br> - Code used to construct and train the CNN is in Owl_CNN_train_model.py<br> - Code to generate spectrograms with randomized parameters based on tagged calls in audio files is in Owl_CNN_generate_training_data.py<br> - Code to generate random spectrograms from a set of audio files (used to generate training data for the Noise class) can be generated with Owl_CNN_make_noise_data.py<br> - Code used to process raw audio files, including segmenting them into 12 s clips, generating spectrograms, and generating class scores using a pre-trained CNN is in Owl_CNN_process_audio.py<br> - Code to generate class scores for an existing set of spectrogram images using a pre-trained CNN are in Owl_CNN_process_images.py</p> <p>Our seven target classes are as follows:<br> AEAC - Northern saw-whet owl, Aegolius acadicus.<br> BUVI - Great horned owl, Bubo virginianus.<br> GLGN - Northern pygmy-owl, Glaucidium gnoma.<br> MEKE - Western screech-owl, Megascops kennicottii.<br> STOC - (Northern) spotted owl, Strix occidentalis caurina.<br> STVA - Barred owl, Strix varia.<br> Noise - Catch-all for any clip that did not contain vocalizations of at least one of the six owl species listed above.</p> <p>The CNN was trained for 100 epochs and saved only after epochs in which validation loss improved. Loss was measured as categorical cross-entropy. The CNN was last saved at epoch 97 with reported metrics:<br> Training loss = 0.218<br> Training accuracy = 0.972<br> Validation loss = 0.165<br> Validation accuracy = 0.987</p> <p>Although this code has been tested and works on our system, we make no guarantee that it will work for others without modification. Created using Python version 2.7.14, TensorFlow version 1.2.1, Keras version 2.2, and SoX version 14.4. Code was developed by Bharath Padmaraju, Zack Ruff, and Chris Sullivan. Questions and comments may be directed to zjruff at gmail dot com.</p> <p>Zack Ruff<br> 15 July 2019</p>
Vocal coordination of provisioning in Black Phoebes (Sayornis nigricans)
<p>Videos were recorded using a Canon FS40 video camera. </p> <p>Supplemental Video 1. This video shows a Black Phoebe approach its nest while making the Wee call. The other parent is not brooding at the time of the provisioning parent’s arrival. The parent feeds the young and makes the Wee call again while flying away from the nest.</p> <p>Supplemental Video 2. This video shows a Black Phoebe approach its nest while making the Wee call. Its mate is brooding at the time of the provisioning parent’s arrival and departs before the provisioning parent arrives. The parent feeds the young and makes the Wee call again while flying away from the nest.</p> <p>Supplemental Video 3. This video shows a Black Phoebe approach its nest while its mate is brooding. The approaching parent makes the Wee call at the moment that it arrives at the nest, but the brooding parent does not leave the nest. The approaching parent then flies away without feeding the young.</p>
A small vocal repertoire during the breeding season expresses complex behavioral motivations and individual signature in the Common Coot
<p><b>Backgroun</b><b>d:</b> Although acoustic communication plays an essential role in the social interactions of Rallidae, our knowledge of how Rallidae encode diverse types of information using simple vocalizations is limited. We recorded and examined the vocalizations of a Common Coot (<i>Fulica atra</i>) population during the breeding season to test the hypotheses that 1) different call types can be emitted under different behavioral contexts, and 2) variation in the vocal structure of a single call type may be influenced both by behavioral motivations and individual signature. We measured a total of 61 recordings of 30 adults while noting the behavioral activities in which individuals were engaged. We compared several acoustic parameters of the same call type emitted under different behavioral activities to determine how frequency and temporal parameters changed depending on behavioral motivations and individual differences.</p> <p><b>Results: </b>We found that adult Common Coots had a small vocal repertoire, including 4 types of call, composed of a single syllable that was used during 9 types of behaviors. The 4 calls significantly differed in both frequency and temporal parameters and can be clearly distinguished by discriminant function analysis. Minimum frequency of fundamental frequency (F<sub>0min</sub>) and duration of syllable (T) contributed the most to acoustic divergence between calls. Call <i>a</i> was the most commonly used (in 8 of the 9 behaviors detected), and maximum frequency of fundamental frequency (F<sub>0max</sub>) and interval of syllables (TI) contributed the most to variation in call <i>a</i>. Duration of syllable (T) in a single call <i>a</i> can vary with different behavioral motivations after individual vocal signature being controlled.</p> <p><b>Conclusions:</b> These results demonstrate that several call types of a small repertoire, and a single call with function-related changes in the temporal parameter in Common Coots could potentially indicate various behavioral motivations and individual signature. This study advances our knowledge of how Rallidae use "simple" vocal systems to express diverse motivations and provides new models for future studies on the role of vocalization in avian communication and behavior.</p>
High plasticity in marmoset monkey vocal development from infancy to adulthood
<p>The vocal behavior of human infants undergoes dramatic changes across their first year, while becoming increasingly speech-like. Surprisingly, vocal development in nonhuman primates has been assumed to be largely predetermined and completed within the first postnatal months. Contradicting this assumption, we found a dichotomy between the development of call features and vocal sequences in marmoset monkeys suggestive of a role for experience. While changes in call features were related to physical maturation, sequences of and transitions between calls remained flexible until adulthood. As in humans, marmoset vocal behavior developed in stages correlated with motor and social development stages. These findings are evidence for a prolonged phase of plasticity during marmoset vocal development, a crucial primate evolutionary preadaptation for the emergence of vocal learning and speech.</p>
Figure 6 in Morphological, vocal and genetic divergence in the Cettia acanthizoides complex (Aves: Cettiidae)
Figure 6. One song strophe of Cettia a. concolor, Taiwan, May; tape recording by Per Alström.
Allo-preening is linked to vocal signature development in a wild parrot
<p>Allo-grooming networks in primate social groups are thought to have favored the evolution of vocal recognition systems, including vocal imitation in humans, as a more effective means of maintaining social bonds in large groups. Select avian taxa converged on vocal learning, but it is not clear what role analogues of allo-grooming might have played. Unlike allo-grooming in most primates, allo-preening in birds is usually limited to pair-bonds. One exception to this is during nestling development when siblings preen each other, but it is unknown how allo-preening influences vocal learning. We addressed this question in wild Green-rumped Parrotlets (<i>Forpus passerinus</i>) in Venezuela. Nestlings learn signature contact calls from adult templates. Large broods, age hierarchies and protracted development in this species create the potential for complex allo-preening networks and a unique opportunity to test how early sociality makes the development of vocal learning labile. From audio-video recordings inside nest cavities and a balanced design of different brood sizes, we quantified allo-preening interactions between marked nestlings, to compare to signature contact calls. Controlling for brood size and age hierarchy, the propensity to preen a larger number of individuals (i.e., out-strength) correlated positively with the age at first contact call. Allo-preening and acoustic similarity matrices did not reveal clear correlations within broods, instead larger broods produced greater contact call diversity. Results indicate that allo-preening elongates the period during which contact calls develop, which might allow individuals time to form a unique signature under the computationally challenging social conditions inherent to large groups.</p>
AVP-LVT Vocal Percussion Dataset
<p>The <strong>AVP-LVT dataset</strong> contains vocal percussion utterances from two publicly available datasets: the personal subset of the <a href="https://dl.acm.org/doi/abs/10.1145/3356590.3356844">Amateur Vocal Percussion (AVP) dataset</a> and the third subset of the <a href="https://repositorio-aberto.up.pt/handle/10216/105309">Live Vocalised Transcription (LVT) dataset</a>. They contain vocal percussion sounds from a total of 48 participants with little or no experience in beatboxing.</p> <p>The <strong>AVP dataset</strong> contains a total of 4873 vocal percussion sound events recorded by 28 participants and with four annotated labels: kick drum, snare drum, closed hi-hat, and opened hi-hat. For each participant, four files contain repetitions of vocal percussion sounds of the same class and one file corresponds to a freestyle improvisation with these. The <strong>LVT dataset</strong> contains a total of 841 vocal percussion sound events recorded by 20 participants with three annotated labels: kick drum, snare drum, and closed hi-hat. For each participant, one file contains a predictable beatbox-style phrase repeated four times and another file contained a freestyle improvisation with the sounds used in the phrase file.</p> <p>This dataset expands the original annotations of both datasets (onsets and instrument labels) so as to include <em>syllabic annotations</em> of vocal percussion sounds. These annotations are composed of a first <em>onset phoneme</em>, usually plosive or fricative, and a second <em>coda phoeneme</em>, usually a vowel, a breath sound ("h"), or silence ("x"). These phonemes were annotated following notation conventions from the International Phonetic Alphabet (IPA).</p> <p>The files included here are the recordings and the annotations of the AVP dataset and the annotations of the LVT dataset. To incorporate the audio files from the LVT dataset, please follow the steps outlined in the file entitled "Instructions_to_build_AVP-LVT_Dataset.rtf". This file also contains information about the train-evaluation split for research purposes.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.