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17 results for “Vocal Recording”
Underwater sounds, including killer whale and humpback whale vocalizations, recorded in northern Norway in January 2023
<p>Dataset of underwater acoustic recordings obtained during the expedition “Orcalize” that took place in Skjervøy in northern Norway from 29<sup>th</sup> December 2022 till 6<sup>th</sup> January 2023. The data contains vocalizations from killer whales and songs from humpback whales which gather in the local fjords during the winter months to feed on herring. We recorded in the band of 20 Hz – 60 kHz with calibrated hydrophones arranged in a compact tetrahedral array that we deployed over board of a motorboat. In total we provide 16 files of continuous recordings with duration from several minutes to over one hour. The total dataset is about 7 hours 37 minutes long and the memory size is 62.8 GB. See the file info.pdf for more information.</p>
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>
Fin whale vocalizations recorded at OBS station BS080 in the northeast Pacific Ocean
<p>Fin whale calls recorded by seismic stations in the northeast Pacific sped up 10 times to be audible to humans. </p>
Recordings of zebra finch group behaviors with manually annotated vocal segments
<div> <div>This dataset contains recording files and manually annotated vocal segments of freely behaving zebra finches in the BirdPark. The files contain animal-borne accelerometer, microphone and video channels. Birds are housed in groups of different sizes (2, 4, or 8 birds, (one 7-mins file per group). The dataset contains 112 mins of recordings from 4 different experiments.</div> </div> <p>The dataset is published as an appendix of the following paper:</p> <p><a href="https://doi.org/10.1101/2022.09.23.509166">Rüttimann, L., Wang, Y., Rychen, J., Tomka, T., Hörster, H., Rocha, M. D., & Hahnloser, R. H. (2024). Multimodal system for recording individual-level behaviors in songbird groups. bioRxiv.</a></p> <p>See the README.pdf file for a detailed description.</p>
Vocalization data and scripts to model reindeer rut activity using on-animal acoustic recorders and machine learning
Open the record for dataset details and reuse information.
Dactylomys boliviensis vocalization recording files
<p><strong>Audio recordings of the Bolivian Bamboo Rat</strong><br>These recordings were used for the research titled: <em>"</em>Vocalization and acoustic activity pattern of <em>Dactylomys boliviensis </em>from southwestern Amazon through passive acoustic monitoring.<em>"</em> Metadata for the audio files is available in Supplementary Data S1 associated with the manuscript.</p>
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).
Data from: An archive of longitudinal recordings of the vocalizations of adult Gombe chimpanzees
Studies of chimpanzee vocal communication provide valuable insights into the evolution of communication in complex societies, and also comparative data for understanding the evolution of human language. One particularly valuable dataset of recordings from free-living chimpanzees was collected by Frans X. Plooij and the late Hetty van de Rijt-Plooij at Gombe National Park, Tanzania (1971–73). These audio specimens, which have not yet been analysed, total over 10 h on 28 tapes, including 7 tapes focusing on adult individuals with a total of 605 recordings. In 2014 the first part of that collection of audio specimens covering the vocalizations of the immature Gombe chimpanzees was made available. The data package described here covers the vocalizations of the adult chimpanzees. We expect these recordings will prove useful for studies on topics including referential signalling and the emergence of dialects. The digitized sound recordings were stored in the Macaulay Library and the Dryad Repository. In addition, the original notes on the contexts of the calls were translated and transcribed from Dutch into English.
Data from: Longitudinal recordings of the vocalizations of immature Gombe chimpanzees for developmental studies
Many researchers are interested in chimpanzee vocal communication, both as an important aspect of chimpanzee social behavior and as a source of insights into the evolution of human language. Nonetheless, very little is known about how chimpanzee vocal communication develops from infancy to adulthood. The largest dataset of audiorecordings from free-living immature chimpanzees was collected by the late Hetty van de Rijt-Plooij and Frans X. Plooij at Gombe National Park, Tanzania (1971–1973). These recordings have not yet been analysed. Therefore, the most extensive effort to study the development of chimpanzee vocalizations remains unfinished. The audiospecimens total over 10 h on 28 tapes, including 20 tapes focusing on 17 specific immature individuals with a total of 1,136 recordings. In order to make this dataset available to more researchers, the analogue sound recordings were digitized and stored in the Macaulay Library and the Dryad Repository. In addition, the original notes on the contexts of the calls were translated and transcribed from Dutch into English.
Human vocalization corpus: recordings of infant-directed and adult-directed speech and song in 21 societies
<p>This repository contains a corpus of 1615 audio recordings of speech and song collected in 21 societies, first reported in Moser et al. (2020; <a href="https://www.biorxiv.org/content/10.1101/2020.04.09.032995v5">bioRxiv</a>) and later published in Hilton & Moser et al. (2022; <a href="https://doi.org/10.1038/s41562-022-01410-x">Nature Human Behaviour</a>). For assistance using any of this, contact Cody Moser (<a href="mailto:cmoser2@ucmerced.edu">cmoser2@ucmerced.edu</a>), Courtney Hilton (<a href="mailto:courtney.hilton@auckland.ac.nz">courtney.hilton@auckland.ac.nz</a>), and Samuel Mehr (<a href="mailto:mehr@hey.com">mehr@hey.com</a>).</p> <p>Two versions of the audio are included: raw audio (`IDS-corpus-raw.zip`) and audio that was edited to prepare the recordings for automatic acoustic feature extraction (`IDS-corpus-edited.zip`). `IDS-textGrids.zip` contains annotation files from Praat's silence detection method, which were manually reviewed for accuracy. These files are used with the audio extraction scripts associated with the project (see code linked in paper) to build the edited audio files.</p> <p>`IDS-fieldsites.csv` contains some fieldsite-level metadata; additional metadata is in the Supplementary Information of the paper.</p> <p>In the two .zip archives, filenames have the format XXXYYZ.wav, where "XXX" is a fieldsite code, "YY" is a participant number, and "Z" is a vocalization type.</p> <p>Fieldsite codes are:</p> <blockquote> <p>MBE: Mbendjele BaYaka<br> HAD: Hadza<br> NYA: Nyangatom<br> TOP: Toposa<br> BEJ: Beijing<br> JEN: Jenu Kurubas<br> MEN: Mentawai Islanders<br> KRA: Krakow<br> LIM: Rural Poland<br> TUR: Turku<br> USD: San Diego<br> TOR: Toronto<br> VAN: Tannese Vanuatuans<br> PNG: Enga<br> WEL: Wellington<br> ARA: Arawak<br> TSI: Tsimane<br> SPA: Sápara & Achuar<br> QUE: Quechua<br> ACO: Afrocolombians<br> MES: Colombian Mestizos</p> </blockquote> <p>Participant numbers are padded integers, starting with 01, and are unique within fieldsites.</p> <p>Vocalization types are:</p> <blockquote> <p>A: infant-directed song<br> B: infant-directed speech<br> C: adult-directed song<br> D: adult-directed speech </p> </blockquote> <p>In a few cases, participants vocalized in a different language than was expected, given the primary language of their fieldsite (e.g., when the participant was multilingual, or if they sang a song that contains multiple languages, as in The Beatles' "Michelle"). The file `IDS-unexpectedLanguages.csv` at <a href="https://github.com/themusiclab/infant-speech-song/blob/main/data/IDS-unexpectedLanguages.csv">https://github.com/themusiclab/infant-speech-song/blob/main/data/IDS-unexpectedLanguages.csv</a> contains an inventory of these examples from the English-speaking fieldsites. This issue only affects a small minority of the recordings, as it was typically avoided by the researchers collecting the recordings. </p>
Data from: Longitudinal recordings of the vocalizations of immature Gombe chimpanzees for developmental studies
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Data from: An archive of longitudinal recordings of the vocalizations of adult Gombe chimpanzees
Open the record for dataset details and reuse information.
Supplementary file 3; A video clip, recorded on an iPhone, showing the male lion Shanto in the enclosure, after just being released from the indoor enclosure exhibiting territorial vocalizations
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A Pilot Study for the Collection Of Vocalized, Individual Digital Cough Sound Recordings to Screen for COVID-19
ClinicalTrials.gov study NCT04864535. IPD Sharing: NO. Countries: 1. Publications: 0.
Recording Facial and Vocal Emotional Productions in Children With Autism as Part of the JEMImE Project
ClinicalTrials.gov study NCT03177590. IPD Sharing: NO. Countries: 1. Publications: 0.
Longitudinal Non-invasive Analysis of VOCAle Cord Function Based on Trans-laryngeal Ultrasound Acquisitions and Voice Recordings
ClinicalTrials.gov study NCT06776393. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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