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.
29
datasets available to search
ShareScore release 0.9.0
Dataset results
29 results for “vocal dataset”
Bioacoustic Dataset of African and Florida Manatee Vocalizations for Machine Learning Applications, 2020-2022
This data package presents a comprehensive acoustic library of manatee vocalizations for machine learning (ML) and classifier development. It includes recordings from two species, African and Florida manatees, sampled across four locations. The species are combined due to the acoustic similarity of their vocalizations, providing a diverse and representative training set for ML algorithms. The dataset consists of 0.5-second WAV clips categorized as either containing manatee vocalizations (MV, n=18,129 clips) or not (Noise, n=23,444 clips). MV clips may include multiple vocalizations or truncated calls. All clips were manually verified by two researchers with expertise in manatee acoustics. Recordings were collected using stationary hydrophones deployed in natural habitats, with variable signal-to-noise ratios (SNR) resulting from changes in distance between the vocalizing manatees and the recorders. Background noise across sites is relatively low, with minimal anthropogenic noise; caution is advised when applying models to noisier environments. No dolphin species are believed to be present at the recording sites, and models trained on this dataset should be used cautiously in dolphin-inhabited regions to avoid false positives. If you use this dataset, please reach out to the listed contacts, we are interested in learning how it supports your work.
Guinea baboon vocalizations dataset automatically extracted with a deep neural network from natural audio recordings
<p><strong>Abstract</strong></p> <p>The data collection process consisted of continuously recording during one month a group of Guinea baboons living in semi-liberty at the CNRS primatology center in Rousset-sur-Arc (France). Two microphones we placed nearby their enclosure to continuously record the sounds produced by the group. A convolutional neural network (CNN) was used on these large and noisy audio recordings to automatically extract segments of sound containing a baboon vocal production by following the method of <a href="https://arxiv.org/abs/2302.07640">Bonafos et al. (2023)</a>. The resulting dataset consists of one-second to several-minute wav files of automatically detected vocalizations segments. The dataset thus provides a wide range of baboon vocalizations produced at all times of the day. It can be used to study vocal productions of non-human primates, their repertoire, their distribution over the day, their frequency, and their heterogeneity. In addition to the analysis of animal communication, the dataset can also be used as a learning base for sound classification models.</p> <p> </p> <p><strong>Data acquisition</strong></p> <p>The data are audio recordings of baboons. The recordings were made with a H6 Zoom recorder, using the included XYH-6 stereo microphone. The sample size is 44100 Hertz, 16 bits. The microphones were placed in the vicinity of the enclosure for one month and recorded continuously on a PC computer. A CNN passed over the data with a sliding window of 1 second and an overlap of 80% to detect the vocal productions of the baboons. The dataset consists of the segments predicted by the CNN to contain a baboon vocalization. Windows containing signal less than one second apart were merged into a single vocalization.</p> <p> </p> <p><strong>Data source location</strong></p> <ul> <li>Institution: CNRS, Primate Facility</li> <li> <p>City/Town/Region: Rousset-sur-Arc</p> </li> <li> <p>Country: France</p> </li> <li> <p>Latitude and longitude for collected samples/data: 43.47033535251509, 5.6514732876668905</p> </li> </ul> <p> </p> <p><strong>Value of the data</strong></p> <ul> <li> <p>This dataset is relatively unique in terms of the quantity of vocalizations available.</p> </li> <li> <p>This massive dataset can be very useful to two types of scientific communities: experts in primatology who study the vocal productions of non-human primates, and experts in data science and audio signal processing.</p> </li> <li> <p>The machine learning research community has at its disposal a database of several dozen hours of animal vocalizations, which will make it possible to build up a large learning base, very useful for Environemental Sound Recognition tasks, for example.</p> </li> </ul> <p> </p> <p><strong>Objective</strong></p> <p>This dataset is a follow-up of two studies on the vocal productions of Guinea baboons (Papio papio) in which we carried out analyses of their vocal productions on the basis of a relatively large vocalization sample containing around 1300 vocalizations (<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169321">Boë, Berthommier, Legou, Captier, Kemp, Sawallis, Becker, Rey, & Fagot, 2017</a>; <a href="https://hal.science/hal-01649539">Kemp, Rey, Legou, Boë, Berthommier, Becker, & Fagot, 2017</a>). The aim was to collect a larger database using the technique of deep convolutional neural networks in order to 1) automatically detect vocal productions in a large continuous audio recording and 2) perform a categorization of these vocalizations on a more massive sample. A description of the pipeline that enabled these automatic detections and categorizations is given in <a href="https://arxiv.org/abs/2302.07640">Bonafos, Pudlo, Freyermuth, Legou, Fagot, Tronçon, & Rey (2023)</a>.</p> <p> </p> <p><strong>Data description</strong></p> <p>The data is a set of audio files in wav format. They are at least one second long (the size of the window), up to several minutes, if several windows are consecutively predicted as containing signal. Moreover, we add the labeled data we used to train the CNN which did the prediction. We also provide two hours of the continuous recordings to have an idea of the continuous recordings and test the code of the paper provided on <a href="https://gitlab.com/papers4375727/detection-and-classification-of-vocal-productions">gitlab</a>.</p> <p>In addition, there is a database in csv format listing all the vocalizations, the day and time of their production, and the prediction probabilities of the model.</p> <p> </p> <p><strong>Experimental design, materials and methods</strong></p> <p>The original recordings represent one month of continuous audio recording. Seven hours of this month were manually labelled. They were segmented and labelled according to whether or not there was a monkey vocalization (i.e., noise or vocalization) and, if there was a vocalization, according to the type of vocalization (6 possible classes: bark, copulation grunt, grunt, scream, yak, wahoo). These manually labelled data were used as a training set for a CNN, which was automatically trained following the pipeline of Bonafos et al. (2023). This model was then used to automatically detect and classify vocalization during the whole month of audio recording. It processes the data in the same way when predicting new data as it does when training. It uses a sliding window of one second with an overlap of 80%. It does not take into account information from previous predictions, but calculates the probability of a vocalization in each one-second window independently. It then iterates through the month. For each window, the model predicts two outputs: the probability that there is a vocalization and the probability of each class of vocalization.</p> <p>For the purpose of generating the wav files, if a window has a probability of a vocalization greater than 0.5, it is considered to contain a vocalization. If it is the first one, a vocalization is started at that moment. If the time windows that follow a vocalization also contain a vocalization, then the signal they contain is added to the first segment for which a vocalization has been detected. As soon as a one-second segment no longer contains a signal corresponding to a vocalization, the wav file is closed. If windows are predicted to contain no vocalizations, but are between two windows that contain vocalizations within 1 second of each other, then all windows are merged.</p>
CatMeows: A Publicly-Available Dataset of Cat Vocalizations
<p><strong>Abstract</strong></p> <p>This dataset, composed of 440 sounds, contains meows emitted by cats in different contexts. Specifically, 21 cats belonging to 2 breeds (Maine Coon and European Shorthair) have been repeatedly exposed to three different stimuli that were expected to induce the emission of meows:</p> <ol> <li><em>Brushing</em> - Cats were brushed by their owners in their home environment for a maximum of 5 minutes;</li> <li><em>Isolation in an unfamiliar environment</em> - Cats were transferred by their owners into an unfamiliar environment (e.g., a room in a different apartment or an office). Distance was minimized and the usual transportation routine was adopted so as to avoid discomfort to animals. The journey lasted less than 30 minutes and cats were allowed 30 minutes with their owners to recover from transportation, before being isolated in the unfamiliar environment, where they stayed alone for maximum 5 minutes;</li> <li><em>Waiting for food</em> - The owner started the routine operations that preceded food delivery in the usual environment the cat was familiar with. Food was given at most 5 minutes after the beginning of the experiment.</li> </ol> <p>The dataset has been produced and employed in the context of an interdepartmental project of the University of Milan (for further information, please refer to this <a href="https://doi.org/10.3390/ani9080543">doi</a>). The content of the dataset has been described in detail in a scientific work currently under review; the reference will be provided as soon as the paper is published.</p> <p><strong>File naming conventions </strong></p> <p>Files containing meows are in the <em>dataset.zip</em> archive. They are PCM streams (.wav).<br> Naming conventions follow the pattern C_NNNNN_BB_SS_OOOOO_RXX, which has to be exploded as follows:</p> <ul> <li>C = emission context (values: B = brushing; F = waiting for food; I: isolation in an unfamiliar environment);</li> <li>NNNNN = cat’s unique ID;</li> <li>BB = breed (values: MC = Maine Coon; EU: European Shorthair);</li> <li>SS = sex (values: FI = female, intact; FN: female, neutered; MI: male, intact; MN: male, neutered);</li> <li>OOOOO = cat owner’s unique ID;</li> <li>R = recording session (values: 1, 2 or 3)</li> <li>XX = vocalization counter (values: 01..99)</li> </ul> <p><strong>Extra content</strong></p> <p>The <em>extra.zip</em> archive contains excluded recordings (sounds other than meows emitted by cats) and uncut sequences of close vocalizations.</p> <p><strong>Terms of use</strong></p> <p>The dataset is open access for scientific research and non-commercial purposes.</p> <p>The authors require to acknowledge their work and, in case of scientific publication, to cite the most suitable reference among the following entries:</p> <p>Ntalampiras, S., Ludovico, L.A., Presti, G., Prato Previde, E., Battini, M., Cannas, S., Palestrini, C., Mattiello, S.: Automatic Classification of Cat Vocalizations Emitted in Different Contexts. Animals, vol. 9(8), pp. 543.1–543.14. MDPI (2019).<br> ISSN: 2076-2615</p> <p>Ludovico, L.A., Ntalampiras, S., Presti, G., Cannas, S., Battini, M., Mattiello, S.: CatMeows: A Publicly-Available Dataset of Cat Vocalizations. In: Li, X., Lokoč, J., Mezaris, V., Patras, I., Schoeffmann, K., Skopal, T., Vrochidis, S. (eds.) MultiMedia Modeling. 27th International Conference, MMM 2021, Prague, Czech Republic, June 22–24, 2021, Proceedings, Part II, LNCS, vol. 12573, pp. 230–243. Springer International Publishing, Cham (2021). <br> ISBN: 978-3-030-67834-0 (print), 978-3-030-67835-7 (online)<br> ISSN: 0302-9743 (print), 1611-3349 (online)</p>
Dataset: Features of animal babbling in the vocal ontogeny of the gray mouse lemur
<p>Dataset used in the unsupervised cluster analysis of the publication "Features of animal babbling in the vocal ontogeny of the gray mouse lemur (<em>Microcebus murinus</em>)"</p> <p><strong>Abstract</strong></p> <p>In human infants babbling is an important developmental stage of vocal plasticity to acquire maternal language. To investigate parallels in the vocal development of human infants and non-human mammals, seven key features of human babbling were defined, which are up to date only shown in bats and marmosets. This study will explore whether these features can also be found in gray mouse lemurs by investigating how infant vocal streams gradually resemble the structure of the adult trill call, which is not present at birth. Using unsupervised clustering, we distinguished six syllable types, whose sequential order gradually reflected the adult trill. A subset of adult syllable types was produced by several infants, with the syllable production being rhythmic, repetitive, and independent of the social context. The temporal structure of the calling bouts and the tempo-spectral features of syllable types became adult-like at the age of weaning. The age-dependent changes in the acoustic parameters differed between syllable types, suggesting that they cannot solely be explained by physical maturation of the vocal apparatus. Since gray mouse lemurs exhibit five features of animal babbling, they show parallels to the vocal development of human infants, bats, and marmosets.</p> <p> </p> <p>For details concerning the recording of the calling bouts confer to the publication at doi:10.1038/s41598-023-47919-7</p>
Dataset: Sex differences in the impact of social relationships on individual vocal signatures in grey mouse lemurs
<p>Dataset used in the statistical analysis of the publication "Sex differences in the impact of social relationships on individual vocal signatures in grey mouse lemurs (<em>Microcebus murinus</em>)"</p> <p><strong>Abstract</strong></p> <p>Vocali<span>z</span>ations coordinate social interactions between conspecifics by conveying information concerning the individual or group identity of the sender. Social accommodation is a form of vocal learning where social affinity is signalled by converging or diverging vocali<span>z</span>ations to those of conspecifics. To investigate whether social accommodation is linked to the social lifestyle of the sender, we investigated sex-specific differences in social accommodation in a dispersed living primate, the grey mouse lemur, where females form stable sleeping groups whereas males live solitarily. We used 482 trill calls of 36 individuals from our captive breeding colony to compare acoustic dissimilarity between individuals with genetic relatedness, social contact time and body weight. Our results showed that female trills become more similar the more time females spen<span>d</span> with each other independent of genetic relationship, suggesting vocal convergence. In contrast, male trills were affected more by genetic than social factors. However, focus<span>s</span>ing only on sociali<span>z</span>ed males, male trills diverged from each other the more time males were cage partners. Thus, grey mouse lemurs show the capacity for social accommodation, with females converging their trills to signal social closeness to sleeping group partners, whereas males do not adapt or diverge their trills to signal individual distinctiveness. </p> <p> </p> <p>For details concerning the recording of the trills confer to the publication at doi: 10.1098/rstb.2023.0193</p>
The UK COVID-19 Vocal Audio Dataset
<p>The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech (speech not available in open access version) were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.</p> <h3>Contents</h3> <ul> <li><strong>participant_metadata.csv</strong> row-wise, participant identifier indexed information on participant demographics and health status. Please see <a href="https://arxiv.org/pdf/2212.07738.pdf">A large-scale and PCR-referenced vocal audio dataset for COVID-19</a> for a full description of the dataset.</li> <li><strong>audio_metadata.csv</strong> row-wise, participant identifier indexed information on three recorded audio modalities, including audio filepaths. Please see <a href="https://arxiv.org/pdf/2212.07738.pdf">A large-scale and PCR-referenced vocal audio dataset for COVID-19</a> for a full description of the dataset.</li> <li><strong>train_test_splits.csv</strong> row-wise, participant identifier indexed information on train test splits for the following sets: 'Randomised' train and test set, Standard' train and test set, Matched' train and test sets, 'Longitudinal' test set and 'Matched Longitudinal' test set. Please see <a href="https://arxiv.org/abs/2212.08570">Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers</a> for a full description of the train test splits.</li> <li><strong>audio/ </strong>directory containing all the recordings in .wav format <ul> <li>Due to the large size of the dataset, to assist with ease of download, the audio files have been zipped into <strong>covid_data.z{ip, 01-24}.</strong> This enables the dataset to be downloaded in short periods, reducing the chances of a dropped internet connection scuppering progress. To unzip, first, ensure that all zip files are in the same directory. Then run the command 'unzip covid_data.zip' or right-click on 'covid_data.zip' and use a programme such as 'The Unarchiver' to open the file.</li> <li>Once extracted, to check the validity of the download, please run the 'python Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers/data-paper/unit-tests.py. All tests should pass with no exceptions. Please clone the GitHub repo detailed below.</li> </ul> </li> <li><strong>README.md</strong> full dataset descriptor.</li> <li><strong>DataDictionary_UKCOVID19VocalAudioDataset_OpenAccess.xlsx </strong>descriptor of each dataset attribute with the percentage coverage.</li> </ul> <h3>Code Base</h3> <p>The accompanying code can be found here: https://github.com/alan-turing-institute/Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers</p> <h3>Citations:</h3> <p>Please cite.</p> <p>@article{coppock2024audio,</p> <p> author = {Coppock, Harry and Nicholson, George and Kiskin, Ivan and Koutra, Vasiliki and Baker, Kieran and Budd, Jobie and Payne, Richard and Karoune, Emma and Hurley, David and Titcomb, Alexander and Egglestone, Sabrina and Cañadas, Ana Tendero and Butler, Lorraine and Jersakova, Radka and Mellor, Jonathon and Patel, Selina and Thornley, Tracey and Diggle, Peter and Richardson, Sylvia and Packham, Josef and Schuller, Björn W. and Pigoli, Davide and Gilmour, Steven and Roberts, Stephen and Holmes, Chris},</p> <p> title = {Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers},</p> <p> journal = {Nature Machine Intelligence},</p> <p> year = {2024},</p> <p> doi = {https://doi.org/10.1038/s42256-023-00773-8}</p> <p>}</p> <p>@article{budd2024,</p> <p> author={Jobie Budd and Kieran Baker and Emma Karoune and Harry Coppock and Selina Patel and Ana Tendero Cañadas and Alexander Titcomb and Richard Payne and David Hurley and Sabrina Egglestone and Lorraine Butler and George Nicholson and Ivan Kiskin and Vasiliki Koutra and Radka Jersakova and Peter Diggle and Sylvia Richardson and Bjoern Schuller and Steven Gilmour and Davide Pigoli and Stephen Roberts and Josef Packham Tracey Thornley Chris Holmes},</p> <p> title={A large-scale and PCR-referenced vocal audio dataset for COVID-19},</p> <p> journal={Scientific Data},</p> <p> year={2024},</p> <p> doi = {https://doi.org/10.1038/s41597-024-03492-w}</p> <p>}</p> <p>@article{Pigoli2022,</p> <p> author={Davide Pigoli and Kieran Baker and Jobie Budd and Lorraine Butler and Harry Coppock and Sabrina Egglestone and Steven G.\ Gilmour and Chris Holmes and David Hurley and Radka Jersakova and Ivan Kiskin and Vasiliki Koutra and George Nicholson and Joe Packham and Selina Patel and Richard Payne and Stephen J.\ Roberts and Bj\"{o}rn W.\ Schuller and Ana Tendero-Ca$\tilde{n}$adas and Tracey Thornley and Alexander Titcomb},</p> <p>title={Statistical Design and Analysis for Robust Machine Learning: A Case Study from Covid-19},</p> <p> year={2022},</p> <p> journal={arXiv},</p> <p> doi = {10.48550/ARXIV.2212.08571}</p> <p>}</p> <p> </p> <h3>The Dublin Core™ Metadata Initiative</h3> <p> </p> <p>- Title: The UK COVID-19 Vocal Audio Dataset, Open Access Edition.</p> <p>- Creator: The UK Health Security Agency (UKHSA) in collaboration with The Turing-RSS Health Data Lab.</p> <p>- Subject: COVID-19, Respiratory symptom, Other audio, Cough, Asthma, Influenza.</p> <p>- Description: The UK COVID-19 Vocal Audio Dataset Open Access Edition is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs and exhalations were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset Open Access Edition represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.</p> <p>- Publisher: The UK Health Security Agency (UKHSA).</p> <p>- Contributor: The UK Health Security Agency (UKHSA) and The Alan Turing Institute.</p> <p>- Date: 2021-03/2022-03</p> <p>- Type: Dataset</p> <p>- Format: Waveform Audio File Format audio/wave, Comma-separated values text/csv</p> <p>- Identifier: <strong>10.5281/zenodo.10043977</strong></p> <p>- Source: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to <a href="https://www.gov.uk/government/publications/accessing-ukhsa-protected-data/accessing-ukhsa-protected-data">Accessing UKHSA protected data</a>.</p> <p>- Language: eng</p> <p>- Relation: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to <a href="https://www.gov.uk/government/publications/accessing-ukhsa-protected-data/accessing-ukhsa-protected-data">Accessing UKHSA protected data</a>.</p> <p>- Coverage: United Kingdom, 2021-03/2022-03.</p> <p>- Rights: Open Government Licence version 3 (OGL v.3), © Crown Copyright UKHSA 2023.</p> <p>- accessRights: When you use this information under the Open Government Licence, you should include the following attribution: The UK COVID-19 Vocal Audio Dataset Open Access Edition, UK Health Security Agency, 2023, licensed under the <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/">Open Government Licence v3.0</a> and cite the papers detailed above.</p> <p> </p>
Amateur Vocal Percussion Dataset
<p>Dataset of vocal imitations of percussion sounds. It includes a total of 9780 utterances in 280 audio files recorded by 28 participants with little or no experience in beatboxing. Onsets and labels for kick drum, snare drum, closed hi-hat, and opened hi-hat are annotated in separate .csv files.</p> <p>All audio files were recorded with a MacBook Pro's built-in mic in an indoor environment and they are distributed into two modalities: personal imitations and fixed imitations. For each modality and participant, four files contain repetitions of utterances of the same class and one file corresponds to a rhythmic improvisation with these.</p> <p><strong>Update from V3</strong>:<em> Onset and coda phoneme labels added, sample noise audio file added.</em></p>
Extreme Metal Vocals Dataset (EMVD)
<p><strong>Extreme Metal Vocals Dataset (EMVD)</strong></p> <p>Version 1.0, October 2023</p> <p> </p> <p><strong>Created by</strong></p> <p>Modan Tailleur (1,3), Julien Pinquier (2), Laurent Millot (1), Corsin Vogel (1), Mathieu Lagrange (3)</p> <ol> <li> <p>ENS Louis-Lumière, Saint-Denis, France</p> </li> <li> <p>IRIT, Université de Toulouse, CNRS, UT3 Toulouse, France</p> </li> <li> <p>Nantes Université, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, France</p> </li> </ol> <p> </p> <p><strong>Publication</strong></p> <p>If using this data in an academic work, please reference the DOI and version, as well as cite the following paper, which presented the data collection procedure and the first version of the dataset:</p> <p>@misc{tailleur2024emvddatasetdatasetextreme,<br> title={EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal}, <br> author={Modan Tailleur and Julien Pinquier and Laurent Millot and Corsin Vogel and Mathieu Lagrange},<br> year={2024},<br> eprint={2406.17732},<br> archivePrefix={arXiv},<br> primaryClass={cs.SD},<br> url={https://arxiv.org/abs/2406.17732}, <br>}</p> <p> </p> <p><strong>Description</strong></p> <p>The Extreme Metal Vocals Dataset (EMVD) comprises a collection of recordings of extreme vocal techniques performed within the realm of heavy metal music. The dataset consists of 760 audio excerpts of 1 second to 30 seconds long, totaling about 100 min of audio material, roughly composed of 60 minutes of distorted voices and 40 minutes of clear voice recordings. These vocal recordings are from 27 different singers and are provided without accompanying musical instruments or post-processing effects. The distortion taxonomy within this dataset encompasses four distinct distortion techniques and three vocal effects, all performed in different pitch ranges.</p> <p> </p> <p><strong>How to use</strong></p> <p>To get an example on how to use this dataset for deep learning applications, please follow the link to the companion website: <a href="https://github.com/modantailleur/ExtremeMetalVocalsDataset">https://github.com/modantailleur/ExtremeMetalVocalsDataset</a></p> <p> </p> <p><strong>Label Taxonomy</strong></p> <p>The label taxonomy is as follows (see our paper for further details):</p> <p>Techniques:</p> <ul> <li>Clear Voice: high, mid, low</li> <li>Black Shriek: high, mid</li> <li>Death Growl: mid, low</li> <li>Hardcore Scream: high, mid, low</li> <li>Grind Inhale</li> </ul> <p>Effects:</p> <ul> <li>Pig Squeal</li> <li>Deep Gutturals</li> <li>Tunnel Throat</li> </ul> <p> </p> <p><strong>Recording procedure</strong></p> <p>For the recording sessions, a mobile setup was selected to accommodate as many singers as possible. An SM58 microphone was employed, chosen for its prevalence as a microphone commonly used by metal singers during live performances. A closed-back headphone served for music playback and provided the singers with a monitor of their own voice if they desired to hear it during recording. An audio interface <em>Scarlett 6i6</em> by <em>Focusrite</em> was responsible for connecting the laptop, microphone, and headset.</p> <p>In some cases, singers were recorded remotely using their own equipment (a stage microphone and an audio interface) which are documented in the database. These singers were provided with a video tutorial and explanatory documents to facilitate their participation in the project, with the main author remotely guiding them. Each singer was instructed to sustain three vowels—[a] as in "cat," [i] as in "ship," and [u] as in "book"—for a duration of five seconds each. They were required to maintain a consistent pitch not only within each vowel but also across all vowels produced. After this, they were asked to perform for approximately 15 seconds using the same vocal technique, but this time with lyrics of their choosing. The lyrics had to remain the same across all technique categories. Each vocal technique was recorded across several registers (high, mid, and low) depending on their relevance to the specific technique. It's worth noting that the Grind Inhale technique, although producible in multiple registers, was recorded in only one register, as many singers deemed it potentially harmful to their voice. A musical loop was provided in the singers' headphones during each recording.</p> <p> </p> <p><strong>Grading system</strong></p> <p>Each vocalist in this study underwent a comprehensive assessment of their comfort level with each vocal technique across the various vocal registers, employing a ranking system ranging from 0 to 5. A rank of 0 signifies that they never use this technique and are not sufficiently comfortable to produce it, which ultimately results in missing data in the dataset. A rank of 3 indicates occasional use, and a rank of 5 signifies that they use it in every live performance.This dataset provides supplementary insights into the singers' practices. These include the typical microphone-to-mouth distance employed by each vocalist during recording, as well as their professional status within the field of singing. The majority of the recordings were conducted onsite, within the familiar confines of the vocalist's chosen location, whether it be their home or a professional studio, utilizing equipment provided by the authors. However, some recordings were independently done by the vocalists themselves, leveraging their personal microphones and audio interfaces. In such instances, the authors remotely guided the recording process to ensure consistency and quality. Detailed equipment specifications have been documented.</p> <p>As authors noticed that the singers auto-evaluation ranking wasn’t very effective, the main author provided grades to individual audio files created by the singers, ranging from 0 to 2. A 2 grade suggests that the technique closely represents the intended vocal technique, 1 indicates that it moderately represents the vocal technique, and 0 signifies that the technique does not adequately represent the vocal technique. Audio files rated as 0 should not be employed for deep learning applications, but they are retained within the dataset in case future re-evaluation of the audio files is desired. Notably, approximately 70\% of the dataset's audio files received grades of 2 or 1 from the authors and are thus suitable for being used in diverse applications.</p> <p> </p> <p><strong>metadata_files.csv</strong></p> <p><em>file_name : </em>the name of the audio file</p> <p><em>singer_id : </em>the id of each singer (from 1 to 27)</p> <p><em>type : </em>whether the distortion employed is a technique, an effect, or a distortion that doesn’t fit any specific category</p> <p><em>name : </em>the name of the technique or of the effect employed by the singer (‘-’ if it doesn’t fit in any category)</p> <p><em>range</em> : the range employed by the singer (‘High’, ‘Mid’, or ‘Low’)</p> <p><em>vowel</em> : the vowel employed by the singer. ‘a’ if vowel [a] as in "cat", 'i' if vowel [i] as in "ship," and 'u' if vowel [u] as in "book"</p> <p><em>authors_rank</em> : the rank given by the authors (2, 1 or 0)</p> <p><em>duration(s) </em>: duration (in seconds) of the audio file</p> <p> </p> <p><strong>metadata_singers.csv</strong></p> <p><em>singer_id</em> : the id of each singer (from 1 to 27)</p> <p><em>gender : </em>the gender of the singer (« M » if male, « F » if female)</p> <p><em>status : </em>whether the singer is professional or non-professional (« Professional », or « Non-professional »)</p> <p><em>recording : </em>whether the recording was made onsite, with the authors equipment, or if it was guided remotely (« Onsite » or « Guided »)</p> <p><em>distance_to_microphone(cm) : </em>the distance chosen by the singer to the microphone (in centimeters)</p> <p><em>microphone : </em>model of microphone that was used for the recording</p> <p><em>audio_interface : </em>audio interface used for the recordings</p> <p><em>DAW : </em>Digital Audio Workstation (DAW) used for recording the singer (Ex: ProTools, Reaper etc...)</p> <p><em>ClearVoice_High, …, TunnelThroat : </em>singer’s rank (from 0 to 5) from his auto-evalution on each technique performed in each range.<br> </p> <p><strong>split_kfolds.csv</strong></p> <p>For deep learning applications, a k-fold cross-validation with 4 folds was performed and stored in the «split_kfolds.csv » file, reserving 20% of the training data for validation.</p> <p><em>file_name : </em><em>the name of the audio file</em></p> <p><em>split0, …, split3</em> : for each split, wether the file belongs to the train subset (‘train’), the evaluation subset (‘eval’), the validation subset (‘valid’) or if it isn’t used for training (‘-’)</p> <p> </p> <p><strong>Feedback</strong></p> <p>Please help us improve EMVD by sending your feedback to:</p> <ul> <li>Modan Tailleur: <a href="mailto:modan.tailleur@gmail.com">modan.tailleur@gmail.com</a></li> </ul> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We want to thank Oriol Nieto, Geoffroy Peeters, Christophe d'Alessandro and Boris Doval for fruitful discussion. We particularly want to thank Joshua Smith for guidance for the design of the taxonomy. We also want to thank the 27 singers for bringing this dataset to life.</p>
Vocal Fold Paralysis Dataset and Code
<p>Data and Code for Low et al. Uncovering the important acoustic features for detecting vocal fold paralysis with explainable machine learning.</p>
Zebra finch dataset for the paper: Benchmarking nearest neighbor retrieval of zebra finch vocalizations across development
<p>This is the dataset created in the paper "Benchmarking nearest neighbor retrieval of zebra finch vocalizations across development".</p>
Dataset - mouse spontaneous ultrasonic vocalizations
<p>This datasets includes all data used in our study " LMT USV Toolbox, a novel methodological approach to place mouse ultrasonic vocalizations in their behavioral contexts – A study in female and male C57BL/6J mice and in Shank3 mutant females". A "readme" file describes the type of analyses based on each data file.</p>
Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets
<p>Acoustic indices combined with clustering and classification approaches have been increasingly used to automate identification of the presence of vocalizing taxa or acoustic events of interest. While most studies using this approach standardize data collection and study design parameters at the project or study level, recent trends in ecological research are to investigate patterns at regional or continental scales. Large-scale studies often require collaboration between research groups and integration of data from multiple sources to fulfill objectives, which can lead to variation in recording equipment and data collection protocols.</p> <p>Our objectives were to determine how analytical approaches and variation in data collection and processing that is typical of regional acoustic monitoring programs influences accuracy when identifying vocal activity in migratory breeding birds. We used data from three regional datasets in Northern Alberta, Northern British Columbia, and Southern and Central Yukon, Canada to investigate the effect of analytical framework, sample size, local species richness, and data collection variables on classification accuracy.</p> <p>We found supervised classification approaches to be the most effective, with boosted regression trees identifying vocal activity with a 92.0% accuracy and easily able to accommodate variation in data collection and processing parameters. We also provide recommendations on effectively processing large and heterogeneous datasets including sufficient sample size, accommodating nuisance variables, and selecting suitable model training data.</p> <p>The results presented in this study can help inform decisions in data collection, data processing, and study design and analysis, maximize performance and accuracy during analysis, and efficiently process large, heterogeneous datasets to answer questions at scales previously difficult to investigate.</p>
USCMed Dataset - Rat Ultrasonic Vocalizations
<p>The USCMed dataset is included as an open-access dataset in the paper, "A Reliable and Efficient Detection Pipeline for Rodent Ultrasonic Vocalizations" which was published at the 7th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2025). This dataset was generated from experiments (approved by the University of South Carolina Institutional Animal Care and Use Committee) conducted at the University of South Carolina School of Medicine.</p> <p>The USCMed dataset contains audio recordings and hand-scored annotations of 27 male rats for the Context trial (details in the paper). Note that, there are empty CSV files in the annotations directory for the audio recordings where no USV calls were found by human experts.</p>
ReCANVo: A Dataset of Real-World Communicative and Affective Nonverbal Vocalizations
<p>A dataset of 7077 labeled vocalizations made by non-speaking individuals. Each vocalization lasts approximately 0.5-4 seconds and is labeled with its affective or communicative meaning. Data were acquired in real-world settings (homes, schools, etc.) and were labeled in real-time by parents or caregivers who knew the non-speaking communicator well. </p> <p>dataset_file_directory.csv provides the name of each vocalization file, the corresponding participant ID, and the vocalization meaning or label (delighted, frustrated, request, etc.).</p> <p>If you use this dataset, please <strong>cite Johnson & Narain et al., "ReCANVo: A Database of Real-World Communicative and Affective Nonverbal Vocalizations"</strong>. The authors are Jaya Narain, Kristina T. Johnson, Thomas Quatieri, Pattie Maes, and Rosalind Picard. This paper provides more information about the dataset, including data acquisition methodology, pre-processing procedures, and participant demographics. </p> <p>**J.N. and K.T.J. are joint first authors on this project. Please include both names in attribution when possible (e.g., Johnson & Narain et al.).</p>
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>
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>
Testing Context-Aware Software Systems in the Automotive Domain: A Multi Vocal Literature Review Protocol and Dataset
<h2>Testing Context-Aware Software Systems in the Automotive Domain: A Multi Vocal Literature Review Protocol and Dataset</h2><p>A Multi-Vocal Literature Review (MVLR) is a form of a systematic review that includes grey literature in addition to peer-review literature (Garousi, Felderer, and Mäntylä 2019). The decision to justify an MVLR is drawn from the results of recent literature reviews, in particular the recent results from (Matalonga et al. 2022) where it is shown that there is little evidence in the white literature on the approaches to testing non-academic CASS Systems.</p><p>Previous academic works (including our Quasi-Systematic Literature Reviews and Rapid Reviews) operate under the following assumptions and observations:</p><ul><li>Assumption 1. CASS are widespread and being deployed for commercial or industrial use.</li><li>Observation 1. The software engineering and software testing communities have had time to adopt (or develop new) techniques to deal with the context and effects of testing software systems.</li><li>Observation 2. Academics have been able to work with software organizations to transfer or study the approaches used to test CASS, yet the published case studies we are aware of describe a partial picture of the overall adoption and approach of the problem.</li></ul><p>In spite of these assumptions and the availability of systematic literature review studies, there is little evidence of how software organizations are testing CASS.</p><h3>Research Goals</h3><h4><strong>Aim: </strong>To uncover evidence on how the automotive industry reports their working with the dynamic testing process regarding CASS.</h4><p>We use the term industry to broaden our scope to include stakeholders with an interest in the quality of CASS like NGOs, standard-setting organizations and regulation-setting organizations who can influence how software must be treated in different domains.</p><p>The following research questions convey the general interest of our enquiries. These are driven by our previous research and expectations on the sources.</p><ul><li><strong>RQ1</strong> Are there sources to support the understanding and indicate directions to deal with the problem of testing CASS?</li><li><strong>RQ2</strong> What are the challenges of using these dynamic testing process solutions?</li><li><strong>RQ3 </strong>How are the dynamic testing processes that deal with the context of CASS described in the sources?</li></ul><h3>Dataset </h3><p>This dataset contains the following artifacts:</p><ol><li><strong>Testing CASS MVLR Protocol.pdf</strong>: protocol containing the methodological details of performing the MVLR</li><li><strong>Sources Identification, Selection and Data Extraction.xlsx</strong>: spreadsheet used to record and control discovered and selected sources</li><li><strong>Extraction_Documents.zip</strong>: compressed file containing all data collection forms filled with data extracted from the</li><li><strong>MVLR_Analysis-Codebook.xlsx</strong>: listing of codes emerging from the collected data</li></ol><p> </p>
Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets
Open the record for dataset details and reuse information.
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>
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.