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693 results for “Vocalization”
Figure 5 in Evidence for vocal diversity during physical interference at the perch in sympatric Carollia species (Chiroptera: Phyllostomidae): a key to social organization and species coexistence?
Figure 5. Cluster analysis of class usage per dyad during physical interference at the perch, based on Euclidean distances. Each symbol represents a specific dyad. The x-axis represents the three clusters set by k-means clustering to which a given dyad was sorted; the y-axis represents the species to which a given dyad belonged, and the z-axis represents the relative distances of each dyad from the respective cluster centre. Dyad sex composition (passive bat is given second) is indicated by different symbols, with the sex of the passive bat indicated by different colours (in red–pink dyads, the passive bat is female; in blue–turquoise dyads it is male). Note that most dyads of a given species grouped in a specific cluster, whereas no clear pattern was found for dyad sex composition or sex of the passive bat.
Figure 4 in Evidence for vocal diversity during physical interference at the perch in sympatric Carollia species (Chiroptera: Phyllostomidae): a key to social organization and species coexistence?
Figure 4. The frequency of occurrence of a class across interactions is represented by different colours for Carollia castanea (Cc), Carollia sowelli (Cs) and Carollia perspicillata (Cp). Of the 21 classes discriminated, 20 occurred in C. castanea, 12 in C. perspicillata and five in C. sowelli. The high vocal variability of C. castanea is highlighted by the presence of six rarely occurring classes specific for this species, summarized as other. Please note that class dms, selected for comparative analyses, occurred frequently across all species.
Figure 3 in Evidence for vocal diversity during physical interference at the perch in sympatric Carollia species (Chiroptera: Phyllostomidae): a key to social organization and species coexistence?
Figure 3. Oscillograms (upper panels) and sonagrams (lower panels) representing down-sweeps (sensu Knörnschild et al., 2013) emitted by the three sympatric Carollia species present at Hitoy Cerere, Costa Rica. A, C, E, parts of a dms bout of Carollia castanea (A), a dms bout of Carollia sowelli (C) and a dms bout of Carollia perspicillata (E) are given. B, D, F, for comparison, two ds syllables of C. castanea (B), two of C. sowelli (D) and four of C. perspicillata (F) are shown. Note that syllable durations and time intervals between syllables are quasi-constant within dms bouts and more variable for sequences of ds syllables.
Figure 2 in Evidence for vocal diversity during physical interference at the perch in sympatric Carollia species (Chiroptera: Phyllostomidae): a key to social organization and species coexistence?
Figure 2. Oscillograms (upper panels) showing the relative amplitude (rel. amp.) and sonagrams (lower panels) representing typical frequency–time contours of vocalization classes associated with the social interaction of Carollia bats landing on, grabbing or hanging on a perched conspecific: warbles (A), down-sweep-warble (B), U (C), sinus (D), convex downwardmodulated (E), a dms syllable followed by upward-modulated-sweep (F), other_6 (G), other_1 (H) followed by other_2 (I), U-warbles (J), shallow-U (K), other_3 (L) followed by flat-down-sweep (M), sinus-warble (N) and other_4 (O). A and B are examples from Carollia perspicillata; other examples are from Carollia castanea.
Figure 1 in Evidence for vocal diversity during physical interference at the perch in sympatric Carollia species (Chiroptera: Phyllostomidae): a key to social organization and species coexistence?
Figure 1. Phylogenetic tree for species of the genus Carollia. Genus Rhinophylla served as an outgroup. The numbers above branches are posterior probability estimations. Note how the individuals of the study cluster together in the correct species. Carollia perspicillata are indicated in orange, Carollia sowelli in green and Carollia castanea in yellow.
Figure 6 in Evidence for vocal diversity during physical interference at the perch in sympatric Carollia species (Chiroptera: Phyllostomidae): a key to social organization and species coexistence?
Figure 6. Species discrimination based on a discriminant function analysis of acoustic parameters of dms syllables. Median values for each dyad were used in the analysis. The two discriminant functions (DF1 and DF2) are given with the percentage of variance explained. Carollia castanea (Cc) is represented by circles, Carollia sowelli (Cs) by triangles and Carollia perspicillata (Cp) by squares. The corresponding centroids are shown with a bigger symbol. For each species, 95% confidence ellipses are also plotted.
Hennessy et al-Lion vocalization-Supplementary file 3
<p>Video recording of the male lion stalking the observer along the enclosure fence</p>
Hennessy et al-Lion vocalization-Supplementary file 2
<p>Data for the number of lion territorial vocalization bouts recorded each hour</p>
A mechanism for punctuating equilibria during mammalian vocal development
<p>Evolution and development are typically characterized as the outcomes of gradual changes, but sometimes such changes are abrupt: States of equilibrium can be punctuated by sudden change. Here, we studied the early vocal development of three different mammals: common marmoset monkeys, Egyptian fruit bats, and humans. Consistent with the notion of punctuated equilibria, we found that all three species undergo at least one sudden transition in the acoustics of their developing vocalizations. This was expected. To understand the mechanism, we modeled different developmental landscapes. We found that the transition was best described as a shift in the balance of two vocalization landscapes. We show that the natural dynamics of these two landscapes are consistent with the dynamics of energy expenditure and information transmission. By using them as constraints for each species, we predicted the differences in transition timing from immature to mature vocalizations. Using marmoset monkeys, we were able to manipulate both infant energy expenditure (vocalizing in a heliox environment) and information transmission (closed-loop contingent parental vocal playback). These experiments supported our predictions regarding the role of energy and information in leading to punctuating equilibrium states of vocal development.</p>
Early postnatal individual vocal recognition in a highly colonial mammal species
<p>Dataset associated with the article entitled 'Early postnatal individual vocal recognition in a highly colonial mammal species'</p>
Data from: Whistling shares a common tongue with speech: bioacoustics from real-time MRI of the human vocal tract
Most human communication is carried by modulations of the voice. However, a wide range of cultures has developed alternate forms of communication that make use of a whistled sound source. For example, whistling is used as a highly salient signal for capturing attention, can have iconic cultural meanings such as the cat-call, enact a formal code as in boatswain's calls, or stand as a proxy for speech in whistled languages. We used real-time magnetic resonance imaging to examine the muscular control of whistling to describe a strong association between the shape of the tongue and the whistled frequency. This bioacoustic profile parallels the use of the tongue in vowel production. This is consistent with the role of whistled languages as proxies for spoken languages, in which one of the acoustical features of speech sounds are substituted with a frequency modulated whistle. Furthermore, previous evidence that non-human apes may be capable of learning to whistle from humans suggests that these animals may have similar sensorimotor abilities to those that are used to support speech in humans.
Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology
<p><strong>Abstract</strong></p> <p>The analysis of recorded audio material using computational methods has received increased attention in ethnomusicological research. We present a curated dataset of traditional Georgian vocal music for computational musicology. The corpus is based on historic tape recordings of three-voice Georgian songs performed by the the former master chanter Artem Erkomaishvili. In this article, we give a detailed overview on the audio material, transcriptions, and annotations contained in the dataset. Beyond its importance for ethnomusicological research, this carefully organized and annotated corpus constitutes a challenging scenario for music information retrieval tasks such as fundamental frequency estimation, onset detection, and score-to-audio alignment. The corpus is publicly available and accessible through score-following web-players.</p> <p><strong>License</strong></p> <p>This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.</p> <p><strong>Copyright of Audio (wav)</strong></p> <p>Ministry of Culture, Sports and Youth of Georgia<br> Legal Entity of Public Law<br> Vano Sarajishvili Tbilisi State Conservatoire (TSC)<br> 8-10, GRIBOEDOV St, TBILISI 0108, GEORGIA Tel. / fax :(+995 32) 2 999 144,<br> www.tsc.edu.ge E-mail: info@tsc.edu.ge; inter@tsc.edu.ge</p> <p>We thank the rector of TSC, Nana Sharikadze, for the permission to publish the recordings along with our annotations on Zenodo.</p> <p><strong>Copyright of Annotations (csv)</strong></p> <p>Sebastian Rosenzweig^1, Frank Scherbaum^2, David Shugliashvili^3, Vlora Arifi-Müller^1, and Meinard Müller^1<br> ^1: International Audio Laboratories Erlangen, Germany<br> ^2: University of Potsdam, Germany<br> ^3: Tbilisi State Conservatoire, Georgia</p> <p>The provided digital sheet music in MusicXML-format is based on the transcriptions by David Shugliashvili as published in the book:</p> <p>David Shugliashvili<br> Georgian Church Hymns, Shemokmedi School<br> Georgian Chanting Foundation, 2014.</p> <p><strong>References</strong></p> <p>If you use the Erkomaishvili dataset in your research, please cite:</p> <p>Sebastian Rosenzweig, Frank Scherbaum, David Shugliashvili, Vlora Arifi-Müller, and Meinard Müller<br> Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology<br> Transactions of the International Society for Music Information Retrieval (TISMIR), 3(1): 31–41, 2020.</p>
Dataset of Gunshot Sounds and Koogu Model related to "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" by Yoh et al. 2024
<h2>Description</h2> <p>This repository contains gunshot sound training data, testing data, and the Convolutional Neural Network (CNN) model used in our study on gunhunting patterns in Gabon. The dataset includes hundreds of audio recordings containing gunshots and other environmental sounds, their associated annotations, and the trained Koogu machine-learning model.</p> <h2>Data Summary</h2> <ul> <li><strong>Data Types:</strong> Gunshot sound recordings, non-gunshot sound recordings, annotations, model files. Data are in the format required for Koogu, where training and test data are contained in folders of audio files with associated folders containing the Raven Pro selection tables. </li> <li><strong>Data Format:</strong> <ul> <li>Audio files: .wav</li> <li>Annotations: .txt</li> <li>Model: TensorFlow/Keras model (.h5)</li> <li>Code: .py</li> </ul> </li> </ul> <h2>Data Details</h2> <p><em>Training and Test Data:</em></p> <ul> <li>train_annotations <ul> <li>"Training_Gunshots_Batch1.txt" is a Raven Pro selection table that contains 203 gunshot annotations that were detected through the spectral cross-correlation analysis. </li> <li>"Training_Gunshots_Batch2.txt" is a Raven Pro selection table that contains 223 gunshot annotations that were detected through the initial Koogu model that was run over the whole dataset.</li> <li>"Training_NotGunshots.txt" is a Raven Pro selection table that contains 8,614 non-gunshot annotations, including sounds from branch snaps, tree falls, calls of the putty-nosed monkey, ambient noise, etc.</li> </ul> </li> <li>train_audio <ul> <li>"audio_files" contains 184 audio files associated with the selection table "Training_Gunshots_Batch1.txt"</li> <li>"audio_files_second_batch" contains 203 clips associated with the selection table "Training_Gunshots_Batch2.txt". </li> <li>"other_clips" contains 9,274 clips associated with the selection table "Training_NotGunshots.txt".</li> </ul> </li> <li>test_annotations <ul> <li>"allgunshots_test_gunshots_adjusted_final_clean_20221120.txt" is a Raven Pro selection table that contains the 138 gunshot annotations associated with the files in the "test_audio" folder. This comprises the test dataset. </li> </ul> </li> <li>test_audio<br> <ul> <li>Contains the 131 recordings associated with the 138 gunshot annotations in "allgunshots_test_gunshots_adjusted_final_clean_20221120.txt"</li> </ul> </li> </ul> <p><em>Koogu Model:</em></p> <ul> <li>qDN_4x8_x4_16_Allsites_V3<br> <ul> <li>This is the Koogu model used in this study. It can be run using the script "Koogu_Run_Local_Python_Gabon_ModelV3.py" that you modify depending on the locations of your data and model. </li> </ul> </li> </ul> <p><em>CoLab</em>: An example Koogu CoLab page that contains the code used to train and test the model is available <a href="https://colab.research.google.com/drive/1TerOhzsCs9zSMj9uQ31HX7XPKG1XFYCt?usp=sharing">here.</a></p> <h2>Data Collection</h2> <ul> <li><strong>Collection Method:</strong> BAR-LT audio recorders deployed in 110 sites within Gabonese national parks, logging concessions, and community forests.</li> <li><strong>Time Period:</strong> Recordings from February 2021 to June 2022.</li> <li><strong>Geographical Information:</strong> Specific coordinates provided upon request. </li> </ul> <h2>Data Preparation</h2> <ul> <li><strong>Downsampling:</strong> Recordings were downsampled from 44.1 kHz to 4 kHz.</li> <li><strong>Segmentation:</strong> Audio recordings split into consecutive 2.25-second segments with 1.5 seconds of overlap.</li> <li><strong>Normalization:</strong> Waveform normalized to [-1.0, 1.0].</li> <li><strong>Spectrogram Computation:</strong> Using a 64 ms window length and 50% overlap, trimmed to 10–1200 Hz frequency range.</li> <li><strong>Training Inputs:</strong> Consisted of 584 positive class (gunshots) and 25842 negative class spectrograms.</li> </ul> <h2>Requirements</h2> <ul> <li><strong>Software Requirements:</strong> Python 3.8, TensorFlow 2.5, NumPy, Pandas.</li> <li><strong>Hardware Requirements:</strong> GPU with at least 8GB VRAM recommended.</li> </ul> <h2>References</h2> <ul> <li>For more information about this dataset and the model specifications, please consult "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" (Yoh et al. 2024)</li> </ul> <h2>How to Cite this Dataset:</h2> <ul> <li>If you use this dataset in your work, please cite it: <ul> <li>Gottesman, B. (2024). Dataset of Gunshot Sounds and Koogu Model related to "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" by Yoh et al. 2024 (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11192704" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11192704</a></li> </ul> </li> </ul>
FIGURE 3. A in Vocalizations, tadpole, and natural history of Crossodactylus werneri Pimenta, Cruz & Caramaschi, 2014 (Anura: Hylodidae), with comments on distribution and intraspecific variation
FIGURE 3. A density plot of the first discriminant axis (DAPC) on morphological traits from adult males of Crossodactylus werneri. Note the low discrimination among topotypes (blue) and specimens from Serra das Cabras (red). Six principal component axes were retained and explained 95% of total variance.
FIGURE 5 in Vocalizations, tadpole, and natural history of Crossodactylus werneri Pimenta, Cruz & Caramaschi, 2014 (Anura: Hylodidae), with comments on distribution and intraspecific variation
FIGURE 5. Geographic distribution of Crossodactylus werneri in southeastern Brazil. Red circle: type locality (Itatiaia, between the limits of the Brazilian states of Rio de Janeiro, São Paulo, and Minas Gerais); white circles: localities reported by Pimenta et al. (2014); white triangles: new records (present study) from Mococa (above), and Serra das Cabras (Campinas) and Valinhos (below).
FIGURE 4 in Vocalizations, tadpole, and natural history of Crossodactylus werneri Pimenta, Cruz & Caramaschi, 2014 (Anura: Hylodidae), with comments on distribution and intraspecific variation
FIGURE 4. The tadpole (stage 31) of Crossodactylus werneri. From top to bottom: lateral view; dorsal and ventral views of body; oral disc (bottom right). Specimen from Serra das Cabras, Campinas, São Paulo (lot AAG-UFU 5236).
FIGURE 2 in Vocalizations, tadpole, and natural history of Crossodactylus werneri Pimenta, Cruz & Caramaschi, 2014 (Anura: Hylodidae), with comments on distribution and intraspecific variation
FIGURE 2. (A) Adult male specimens of Crossodactylus werneri in life from Serra das Cabras, Campinas, São Paulo: above— AAG-UFU 0981, SVL 21.7 mm; below—AAG-UFU 0982, SVL 21.6 mm. (B) Adult males of Crossodactylus werneri in dorsal view. Left—Serra das Cabras, Campinas, São Paulo (AAG-UFU 1880; SVL 23.0 mm); right—Parque Nacional do Itatiaia, Rio de Janeiro (topotype ZUEC-AMP 7981; SVL 22.1 mm). (C) Adult males of Crossodactylus werneri from Serra das Cabras (Campinas, São Paulo), depicting variation in size and in the degree of reticulation on the belly (right—AAG-UFU 1875, SVL 21.6 mm; left—AAG-UFU 1878, SVL 25.1 mm).
FIGURE 1 in Vocalizations, tadpole, and natural history of Crossodactylus werneri Pimenta, Cruz & Caramaschi, 2014 (Anura: Hylodidae), with comments on distribution and intraspecific variation
FIGURE 1. (A) From top to bottom: oscillogram of a 29-note advertisement call of Crossodactylus werneri preceded by five isolated notes (second note identified by a red arrow); spectrogram of three median notes and respective oscillogram. Sound file: Crossod_werneriSousasSP1aAAGm671. (B) Spectrogram and respective oscillogram of the territorial call of Crossodactylus werneri. Sound file: Crossod_werneriSousasSP1eAAGm671. Further information on these recordings is provided in Appendix III.
Notes on vocalizations of Brazilian amphibians iv
<p>Raw acoustic data of "<strong>Notes on vocalizations of Brazilian amphibians iv: advertisement calls of 20 Atlantic Forest frog species"</strong></p>
FIGURE 5 in Distribution, Vocalization And Taxonomic Status Of Hypsiboas Roraima And H. Angelicus (Amphibia: Anura: Hylidae)
FIGURE 5: Variation in palpebral membrane pigmentation in Hypsiboas roraima. A: The holotype of Hypsiboas angelicus, EBRG 2733, showing dark spots. B: MHNLS 19656, showing a poorly defined reticulation, better described as small dark spots, white spots and short lines. C: MHNLS 16085, showing a well defined white reticulation. D: ROM 43426, showing pale brown and white spots and poorly defined reticulation.
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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.