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65 results for “audio recordings”
Audio tagging of avian dawn chorus recordings in California, Oregon, and Washington
<p><strong>General Summary</strong></p> <p>This acoustic data collection includes 1,575 5-minute soundscape recordings randomly selected from passive acoustic recordings made at 525 sites during 2022 on federally managed lands in western California, Oregon, and Washington, USA. We fully labeled 141 recordings (11.75 hrs) with 39,717 annotations for 118 sound types, including 58 avian species, two mammalian species, six aggregated biotic sounds, and eight non-biotic sound types. An additional 215 recordings were partially annotated with 1,466 annotations. The remaining unlabeled recordings have been included to facilitate novel research applications and methodological evaluations. Beyond the labeled soundscape recordings, we have included township and range identifications and 38 environmental covariates for each recording location.</p> <p><strong>Data Collection</strong></p> <p>Lesmeister et al. (2021) collected passive acoustic recordings during 2022 in support of long-term monitoring of federally threatened northern spotted owl (<em>Strix occidentalis caurina) </em>populations under the Northwest Forest Plan Effective Monitoring Program (U. S. Fish and Wildlife Service 1990, U. S. Department of Agriculture and U. S. Department of the Interior 1994). These data were collected at 643 hexagons that were randomly selected from a tessellation of 5 km2 hexagons covering the entire range of the northern spotted owl (Northern California, Oregon, Washington) under a selective constraint that hexagons contain ≥ 50 % forest-capable lands (<em>def.</em> forested lands or lands capable of developing closed-canopy forests) and be ≥ 25% federal ownership (Davis et al., 2011).</p> <p>Each hexagon was sampled by four Song Meter 4 (SM4) acoustic recording units (Wildlife Acoustics, Maynard, MA) deployed in a standardized spatial arrangement, such that recorders on a site were placed ≥ 500 m apart and were ≥ 200 m from the edge of the sampling hexagon boundary. Recorders were mounted to small trees (15 – 20 cm diameter at breast height) approximately 1.5 m above the ground and were placed on mid-to-upper slopes and ≥ 50 m from roads, trails, and streams. The SM4 devices each have two built-in omnidirectional microphones with a signal-to-noise ratio of 80 dB, typical at 1 kHz, and a recording bandwidth of 20 Hz – 48 kHz. Each device recorded ~11 hours of audio daily for six weeks from March to August at a sampling rate of 32 kHz. The daily recording schedule included a 4-hour window from two hours before sunrise to two hours after sunrise, a 4-hour window from one hour before sunset to 3 hours after sunset, and 10-minute recordings outside the two longer recording blocks at the start of every hour.</p> <p><strong>Data Sampling</strong></p> <p>The goal of this project was to develop a tagged audio dataset (hereafter project dataset) focused on the avian dawn chorus, which is an ecologically important period for the study of avian behavior (McNamara et al. 1987, Staicer et al. 1996, Zhang et al. 2015) and monitoring avian biodiversity (Bibby et al. 2000), but remains a challenging problem for acoustic classification systems (Duan et al. 2013, Stowell 2022). Passive acoustic monitoring on our sites occurs throughout the day. We filtered the full dataset to recordings collected between May and August during the hour immediately after sunrise. From the recordings meeting our filtering criteria, we randomly selected three 5-minute files from each site, which were assigned ordinal labels 'A, 'B,' or 'C.' The final project dataset comprised 131.25 hours of acoustic data.</p> <p><strong>Annotation Protocol</strong></p> <p>We randomly selected 141 sites from the project dataset and fully annotated each recording at a 2-second resolution. We applied labels to each 2-second window of the selected recordings following a predefined sound phonology library (available in the 'metadata.tsv' file), which concatenated the 2021 eBird taxonomy codes (Clements list; Clements et al. 2022) with standardized sonotype codes that incremented depending on the species repertoire (i.e., 'call_1,' 'song_1,' 'drum_1'). For example, 'herthr_song_1' is the label for Hermit Thrush, song_1. Unknown signals were labeled 'unknown,' and clips with no biotic signals (or noise classes of interest documented in metadata.tsv) were labeled 'empty.' Windows were labeled 'complete' and considered fully annotated when every signal was assigned an annotation. Files were deemed fully annotated when every 2-second window contained the 'complete' label.</p> <p><strong>Environmental Covariates</strong></p> <p>Sampling locations will not be published to afford protections for Federally Threatened or Endangered species which may occur on our sites. However, we provide the State, Township, and Range for each sampling location along with the site-specific values for 38 forest structure, topographic, and climatic environmental covariates developed by the Landscape Ecology, Modeling, Mapping, and Analysis group in the Pacific Northwest (<a href="https://lemma.forestry.oregonstate.edu/data">https://lemma.forestry.oregonstate.edu/data</a>; Ohmann and Gregory 2002). State, Township, and Range values are sufficient to explore geographic variation in species- or community-specific call and song phenology and the extracted environmental covariates may provide useful contextual information for novel machine-learning developments (Liu et al. 2018). </p> <p><strong>Description of Data Format</strong></p> <p>The fully annotated audio files can be accessed by downloading and extracting "annotated_recordings.zip." Partially annotated and non-annotated audio files can be accessed by downloading and extracting "additional_recordings_part_1.zip" or "additional_recordings_part_2.zip." Acoustic file names contain site and replicate indicators, such that file "Site_001_Rep_A.wav' was recorded on site 1 and is the A replicate random draw from the available set of dawn chorus recordings. The site and replicate numbers link to additional recording information in "files.tsv," annotations in "annotations.tsv" and "partial_annotations.tsv," as well as site and replicate specific environmental characteristics in "environmental_characteristics.tsv."</p> <p>Metadata describing sound classes and environmental characteristics can be found in "metadata.tsv," and "environmental_characteristics_metadata.tsv."</p> <p><strong>Acknowledgments</strong></p> <p>Acoustic data collection was funded and collected by the US Forest Service and the US Bureau of Land Management. Annotation work was funded by Google. We would also like to thank the many biologists that collected and processed the data compiled here. The use of trade or firm names in this publication is for reader information and does not imply endorsement by the U.S. Government of any product or service.</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>
Audio recordings of COVID-19 positive individuals from the prospective Predi-COVID cohort study with their ageusia and anosmia status
<p>We uploaded 1636 audio recordings originating from 259 distinct participants in the prospective Predi-COVID cohort study recruited between May 2020 and May 2021. The audios have been converted from their original format into WAV files. The audio name structure integrates the participant ID, the recording date and time of the audio recording, the type of audio (Type 1: reading of a text, Type2: hold the [a] vowel), the original audio format, and the symptomatic status for ageusia and anosmia (1: symptomatic, 0: asymptomatic) as such:</p> <p>predi-covid_{participant}{recording date and time}{type of audio}{original format}{sympyomatic status}.wav</p>
Calling activity of Birds in the White Mountain National Forest: Audio Recordings (2016 and 2018)
We collected 410 10-minute sound recordings of birds in and near the Hubbard Brook Experimental Forest in New Hampshire. Recordings, which encompassed most of the bird breeding season in each of two years, included 130,776 vocalizations from 46 taxa. In the associated publication, we report species lists, rarefaction curves, and vocalization descriptions. We also provide analyses of habitat associations, phenology, and spatial patterning in vocalization activity. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Nyokon word list audio recordings
<p>This deposit contains the original recorded lists of words of the Nyokon language (ISO 639-3: nvo) gathered in the process of writing the following publication: </p> <p>Lovestrand, Joseph. 2011. Notes on Nyokon phonology (Bantu A.45, Cameroon). SIL Cameroon. http://silcam.org/download.php?stid=&folder=documents&file=Nyokon_notes_phonology-Lovestrand_2011.pdf.</p> <p>A PDF copy of the publication is included in the deposit [Notes on Nyokon phonology - Lovestrand (October 2011).pdf]</p> <p>Rough phonetic transcriptions and annotations of the word lists are in SML format in the file: [Nyokon-dekereke.txt]</p> <p>This file was designed in the freeware Dekereke program, available online at: https://casali.canil.ca/</p> <p>The audio recordings can be divided into four categories. </p> <p>1. 1700-item word list</p> <p>Files named with a range of four-digit numbers, e.g. 0001-0005.wav, are the original audio files. The numbers refer to the "SIL comparatige African wordlist". Not all 1700 items were elicted and recorded. </p> <p>Roberts, James & Keith Snider. 2006. SIL comparative African wordlist (SILCAWL). SIL <br> Electronic Working Papers 2006-005. 49. http://www.sil.org/silewp/abstract.asp?ref=2006-005</p> <p>2. Individual words for Dekereke </p> <p>Files name with a single four-digit number followed by an English gloss, e.g. 0001 body, are audio clips taken from the origial recordings so that the clips can be listened to in the Dekereke program. <br> Not all words were clipped for this purpose. The recordings of many words are only availabel in the original recordigns.</p> <p>3. Contrastive Pairs </p> <p>Files named ContrastivePairs followed by a number are files that were recorded later in the analysis to check on assumed and suspected phonemic contrast. Most of the words repeat those found elsewhere. </p> <p>4. Consent </p> <p>There are a number of files recording the oral consent of the Nyokon speakers who participated in the word list elicitation sessions. <br> As acknolwedged in Lovestrand (2011) the Nyokon speaker who participated are:</p> <p>NGOUNG Isaac (chief of Ambann)<br> NGAGNI Amos Jules (chief of Ahoung)<br> AMBANG Maurice<br> EMBOM Pierre<br> ENAM Samuel<br> FOUTH Brice Rodrigue<br> HEU Emmanuel<br> HEU Paul<br> IMBO Hermine Dorothée<br> KAMANDA Jean Achille<br> KIARI André Jules<br> KOUMA Fanny<br> MBIRNANG Thomas Blaise<br> MOUOL Catherine<br> NGAGNI Emmanuel<br> YAMBASA André</p>
Two datasets with user generated audio recordings
<p>We provide two open access datasets of <strong>user generated audio recordings </strong>captured with mobile devices such as smartphones and portable cameras . The provided audio files originate from two different public events, a <strong>musical concert</strong> and a <strong>football match</strong>. Also, for each event, we provide two different types of collections; the original <strong>unorganized</strong> collection of uncompressed audio files and an additional <strong>organized</strong> collection, where the different recordings corresponding to similar parts of the event are grouped into specific folders and time-aligned so they can be played back in unison. The interested researcher is invited to read the accompanying paper "<em>Two open access datasets of user generated audio recordings</em>" for finding out more details about these datasets and the way that they can be useful in the context of research related to the organization and reproduction of user generated content.</p>
zEPHYR - Audio files of recorded and auralized wind turbine noise
<p>Audio files associated with the publication "Wind farm noise prediction and auralization", Andrea P. C. Bresciani, Julien Maillard, Arthur Finez, submitted to Acta Acustica in Dec. 2023.</p> <p>Audio 1: Auralized noise for OC1 and SB1<br>Audio 2: Auralized noise for OC1 and SB2<br>Audio 3: Auralized noise for OC1 and SB3<br>Audio 4: Auralized noise for OC2 and SB1<br>Audio 5: Auralized noise for OC2 and SB2<br>Audio 6: Auralized noise for OC2 and SB3<br>Audio 7: Recorded noise for OC1 and SB1<br>Audio 8: Recorded noise for OC1 and SB2<br>Audio 9: Recorded noise for OC1 and SB3<br>Audio 10: Recorded noise for OC2 and SB1<br>Audio 11: Recorded noise for OC2 and SB2<br>Audio 12: Recorded noise for OC2 and SB3<br>Audio 13: Auralized noise for OC2 and SB2 without amplitude fluctuations</p>
A dataset of environmental audio recordings containing chainsaw events
<p>This audio dataset contains audio recordings (.wav format, 8kHz sampling rate) of various durations acquired using eight Cornel University SWIFT Autonomous Recording Units (ARUs) and corresponding metadata (.textgrid files). The recordings were collected within the Rodopi Mountain-Range National Park, Greece, at different seasons between 2018 and 2019. They are part of longer audio recordings. This audio content was used for the evaluation of a chainsaw sound detection system that was developed for the needs of a research project funded via a Single RTDI State Aid Action ”Research – Create – Innovate” grant (T1EDK-04488), which is co-financed by Greece and the European Union (European Regional Development Fund) as part of the Operational Program ”Competitiveness, Entrepreneurship and Innovation” of the National Strategic Reference Framework (NSRF) 2014-2020.</p> <p>Portions of each uploaded recording contain chainsaw events. The temporal location of these events, originally located manually by human listeners, are marked within the corresponding .textgrid files. Each audio recording with the corresponding textgrid file can be opened using the Praat program (<a href="https://www.fon.hum.uva.nl/praat/">https://www.fon.hum.uva.nl/praat/</a>).</p> <p>If you find this dataset useful please cite the following paper:</p> <p>N. Stefanakis, K. Psaroulakis, N. Simou and C. Astaras, "An open-access system for long-range chainsaw sound detection", in Proceedings of EUSIPCO (2022).</p> <p>The chainsaw sound detection algorithm that was developed is also open access and is available in the form of python code from <a href="https://github.com/spl-icsforth/An-open-access-system-for-long-range-chainsaw-sound-detection">https://github.com/spl-icsforth/An-open-access-system-for-long-range-chainsaw-sound-detection</a></p>
Audio recordings of COVID-19 positive individuals from the prospective Predi-COVID cohort study with their fatigue status
<p>We uploaded <strong>3544 </strong>audio recordings originating from <strong>296 </strong>distinct participants with COVID-19 in the prospective <strong>Predi-COVID cohort study</strong> recruited between May 2020 and May 2021. The audios have been converted from their original format into WAV files and normalized. The audio name structure integrates the participant ID, the recording date and time of the audio recording, the type of audio (Type 1: text reading, Type2: holding the [a] vowel without breathing), the original audio format, the gender (W: women, M: Men), and the <strong>fatigue status</strong> of the participant (1: Fatigue, 0: No fatigue) as such:</p> <p>Predi-COVID_{participant ID}{recording date and time}{type of audio}{original format}{gender}{fatigue status}.wav</p>
Multi-site environmental field recordings for audio event recognition in Upstate NY
<p>The first soundscape is located in a lightly wooded suburban area north of Albany, New York, at about 100 meters above sea level. The surrounding vegetation consists of white pines, spruces, oaks, and maples. The acoustic data was originally collected using a Zoom H2n Handy Recorder in 4-channel mode, yet only data from a single microphone capsule is presented.. Recordings were originally made at 16 bits in Waveform Audio File Format (WAV) at a frequency of 44,100 Hz. This dataset was collected from August 2019 to August 2020.</p> <p><br> The second soundscape is located in a forested area in Lake George, New York, also at about 100 meters above sea level. The surrounding vegetation consists largely of white pines. The microphone array consisted of two H2n Zoom recorders mounted perpendicularly to simulate four-channel recording conditions, yet only data from a single microphone capsule is presented. Recordings were originally made at 256 kbps in MPEG-2 Audio Layer III (MP3) format. This dataset was collected from February 2020 until February 2021.</p> <p>Once all of the audio data was collected, it was segmented into 8 second-long clips, to ensure that both biological and anthropogenic sound sources with longer call lengths were provided with sufficient temporal context, while still achieving reasonable computational efficiency. Each segment was then downsampled to 16 kHz for processing efficiency, allowing each to be represented as a 128,000-sample vector. </p> <p>Sounds</p> <p>ECMK- Eastern chipmunk "chuck"</p> <p>DGBR- dog bark</p> <p>DOWO- Downy woodpecker drum</p> <p>RNFL- rainfall</p> <p>ENGE- engine</p> <p>RCCR- remote control car</p> <p>BUBP- back-up beeper (truck)</p> <p>SREN- siren</p> <p>FFCR- fall field cricket</p> <p>DDCC- dog-day cicada</p> <p>ECMC- Eastern chipmunk "chirp"</p> <p>EGSK- Eastern gray squirrel "kuk"</p> <p>AMRO- American robin</p> <p>AMCR- American crow</p> <p>BLJA- Bluejay</p> <p>NOCA- Northern cardinal</p> <p>BCCH- Black-capped chickadee</p> <p>EAPH- Eastern phoebe</p> <p>BAWW- Black-and-white warbler</p> <p>WBNU- White-breasted nuthatch</p> <p>RBNU- Red-breasted nuthatch</p> <p>**Sounds from site 1 have no specific label tag while sounds from site 2 are specifically labeled with "-site2"</p> <p> </p>
beehive Audio recordings
<p>The beehive audio dataset contains 10,000 audio files.</p> <p>Naming convention: <YYMMDD>-<HHMMSS>-<HIVE_ID>.wav</p> <p>Sample rate: 8000 Hz</p> <p>number of values: 65625</p> <p>length of each recording: 8,203125 s</p> <p>format: WAV</p> <p> </p>
Audio recordings from the opening event for The Art of Ikat: A Cambodian Renaissance exhibition
<p>The Art of Ikat: A Cambodian Renaissance is an exhibition, which took place from 22 February to 31 May 2024 at the Royal Danish Library / University of Copenhagen Library Søndre Campus, Karen Blixens Plads 7, 2300 Copenhagen S.</p> <p>This exhibition sheds light on the formation and subsequent loss of this textile collection and the devastating effects of the Cambodian civil war and Khmer Rouge regime on textiles as material culture and heritage. To this end, the exhibition explores the art and practice of ikat, hol (ហូល) in Khmer, a resist-dyed weaving technique mastered in Cambodia, and the significance of figurative and auspicious motifs used in Buddhist pidan ikat hangings. To exemplify the vitality of Cambodian arts, three artists are invited to create new pieces echoing this lost collection, based on the missing objects’ pictures and descriptions found in the cataloguing records recovered post-conflict at the National Museum of Cambodia. Facing this history of conflict which has led to the tremendous loss of artefacts and ancestral know-how, these creators embrace the vitality and resilience of Cambodian arts relying on the power of making, memory, and imagination.</p> <p>Two talks were recorded during the event:<br>- Opening address by Prof. Eva Andersson Strand, Director of the Centre for Textile Research, University of Copenhagen<br>- Keynote presentation by Dr. Magali An Berthon, postdoctoral fellow at the Centre for Textile Research and curator of The Art of Ikat: A Cambodian Renaissance</p> <p>This event received additional support from the Asian Dynamics Initiative at the University of Copenhagen, the David Fond og Samling and the Design History Society.</p>
Pyramic Dataset : 48-Channel Anechoic Audio Recordings of 3D Sources
<p>The Pyramic Dataset contains recordings done using the<br> <a href="https://github.com/LCAV/Pyramic">Pyramic</a> 48 channel microphone array in an<br> anechoic chamber. The recordings consist of 8 different samples (2x sweeps, 1x<br> noise, 5x speech) repeated at 180 angles (every 2 degrees) and from 3 different<br> heights. The audio samples recorded are</p> <ul> <li>Linear and exponential sweeps</li> <li>Noise sequence</li> <li>2x male and 3x female speech</li> </ul> <p>This dataset allows to evaluate the performance of array processing algorithms<br> on real-life recordings done using MEMS microphones similar to those used in<br> mobile phones with all the non-idealities involved. The dataset is suitable for both 2D<br> and 3D scenarios. By subsampling the 48<br> microphones, a large number of array configurations can be tested. Example of<br> algorithms are:</p> <ul> <li>Direction of arrival (DOA) estimation</li> <li>Beamforming</li> <li>Source separation</li> <li>Array calibration</li> </ul> <p>Another application is the generation of realistic room impulse by combining<br> the impulse responses of microphones from sources at multiple angles with a<br> variant of the image source model.</p> <p>In addition to the raw (compressed or not) and segmented<br> recordings, the impulse responses of all the microphones for every source<br> locations were recovered from the exponential sweep measurements and are<br> distributed together with the dataset. The initial manual measurement of loudspeakers<br> and microphones locations was improved upon using a blind calibration method.</p> <p>This record contains</p> <ul> <li>The compressed recordings (TTA format)</li> <li>Segmented recorded samples</li> <li>Impulse responses</li> <li>Documentation and code (also available on <a href="https://github.com/fakufaku/pyramic-dataset">github</a>)</li> </ul> <p>The raw measurements in wav format are available as a separate <a href="https://zenodo.org/deposit/1209005">record</a> (10.5281/zenodo.1209005).</p> <p>The best way to get started is to only get the documentation and code from <a href="https://github.com/fakufaku/pyramic-dataset">github</a> (a copy is available in pyramic-dataset-doc-d2a456b4.zip) and follow the instructions in the README. The version on github is most up-to-date. If possible, please use that one.</p>
Pyramic Dataset : 48-Channel Anechoic Audio Recordings of 3D Sources (Raw)
<p>The Pyramic Dataset contains recordings done using the<br> <a href="https://github.com/LCAV/Pyramic">Pyramic</a> 48 channel microphone array in an<br> anechoic chamber. The recordings consist of 8 different samples (2x sweeps, 1x<br> noise, 5x speech) repeated at 180 angles (every 2 degrees) and from 3 different<br> heights. The audio samples recorded are</p> <ul> <li>Linear and exponential sweeps</li> <li>Noise sequence</li> <li>2x male and 3x female speech</li> </ul> <p>This dataset allows to evaluate the performance of array processing algorithms<br> on real-life recordings done using MEMS microphones similar to those used in<br> mobile phones with all the non-idealities involved. The dataset is suitable for both 2D<br> and 3D scenarios. By subsampling the 48<br> microphones, a large number of array configurations can be tested. Example of<br> algorithms are:</p> <ul> <li>Direction of arrival (DOA) estimation</li> <li>Beamforming</li> <li>Source separation</li> <li>Array calibration</li> </ul> <p>Another application is the generation of realistic room impulse by combining<br> the impulse responses of microphones from sources at multiple angles with a<br> variant of the image source model.</p> <p>In addition to the raw (compressed or not) and segmented<br> recordings, the impulse responses of all the microphones for every source<br> locations were recovered from the exponential sweep measurements and are<br> distributed together with the dataset. The initial manual measurement of loudspeakers<br> and microphones locations was improved upon using a blind calibration method.</p> <ul> </ul> <p>This record contains only the raw measurements in wav format and archive of the documentation and code.</p> <p>The post-processed data is available in a separate <a href="https://zenodo.org/record/1209563">record</a> that contains:</p> <ul> <li>The compressed recordings (TTA format)</li> <li>Segmented recorded samples</li> <li>Impulse responses</li> <li>Documentation and code (also available on <a href="https://github.com/fakufaku/pyramic-dataset">github</a>)</li> </ul> <p>The best way to get started is to only get the documentation and code from <a href="https://github.com/fakufaku/pyramic-dataset">github</a> and download the data as needed into the unzipped archive. Then follow the instructions in README.md.</p>
BirdVox-scaper-10k: a synthetic dataset for multilabel species classification of flight calls from 10-second audio recordings
<p>BirdVox-scaper-10k: a synthetic dataset for multilabel species classification of flight calls from 10-second audio recordings<br> =============================================================================================<br> Version 1.0, September 2019.</p> <p> </p> <p>Created By<br> -------------</p> <p>Elizabeth Mendoza (1), Vincent Lostanlen (2, 3, 4), Justin Salamon (3, 4), Andrew Farnsworth (2), Steve Kelling (2), and Juan Pablo Bello (3, 4).</p> <p> </p> <p>(1): Forest Hills High School, New York, NY, USA<br> (2): Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA<br> (3): Center for Urban Science and Progress, New York University, New York, NY, USA<br> (4): Music and Audio Research Lab, New York University, New York, NY, USA</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p>Description<br> --------------</p> <p>The BirdVox-scaper-10k dataset contains 9983 artificial soundscapes. Each soundscape lasts exactly ten seconds and contains one or several avian flight calls from up to 30 different species of New World warblers (Parulidae). Alongside each audio file, we include an annotation file describing the start time and end time of each flight call in the corresponding soundscape, as well as the species of warbler it belongs to.</p> <p>In order to synthesize soundscapes in BirdVox-scaper-10k, we mixed natural sounds from various pre-recorded sources. First, we extracted isolated recordings of flight calls containing little or no background noise from the CLO-43SD dataset [1]. Secondly, we extracted 10-second "empty" acoustic scenes from the BirdVox-DCASE-20k dataset [2]. These acoustic scenes contain various sources of real-world background noise, including biophony (insects) and anthropophony (vehicles), yet are guaranteed to be devoid of any flight calls. Lastly, we "fill" each acoustic scene by mixing it with flight calls sampled at random.</p> <p>Although the BirdVox-scaper-10k does not consist of natural recordings, we have taken several measures to ensure the plausibility of each synthesized soundscape, both from qualitative and quantitative standpoints.<br> <br> The BirdVox-scaper-10k dataset can be used, among other things, for the research, development, and testing of bioacoustic classification models.</p> <p>For details on the hardware of ROBIN recording units, we refer the reader to [2].</p> <p>[1] J. Salamon, J. Bello. Fusing shallow and deep learning for bioacoustic bird species classification. Proc. IEEE ICASSP, 2017.</p> <p>[2] V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, and J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection. Proc. IEEE ICASSP, 2018.</p> <p>[3] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016.</p> <p> </p> <p> </p> <p> </p> <p>@inproceedings{lostanlen2018icassp,<br> title = {BirdVox-full-night: a dataset and benchmark for avian flight call detection},<br> author = {Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},<br> booktitle = {Proc. IEEE ICASSP},<br> year = {2018},<br> published = {IEEE},<br> venue = {Calgary, Canada},<br> month = {April},<br> }</p>
Data for: The buzzOmeter system: In situ audio recordings of pollinators in flight
<ol> <li><span>The role of sounds produced by free-flying insects is challenging to research due to technical difficulties in obtaining audio recordings suitable for playback experiments. Experimental studies using flight sounds are needed to understand if buzzes carry information and by whom it is perceived.</span></li> <li><span>We developed the 'buzzOmeter system' for recording untethered, flying insects in their habitat, followed by file processing that allows precise measurements of acoustic parameters, including those dependent on the distance of the sound source from the microphone, i.e. signal magnitude measurements. The system consists of commercially available elements and open-source software.</span></li> <li><span>We provide a practical guide for the assembly and use of two alternative setups of the buzzOmeter system, followed by a video tutorial on file processing and an R script for the assignment of audio recordings to the corresponding species based on mixture discriminant analysis. Recordings of nine insect species (bees, wasps and lepidopterans) obtained with the use of our system in various habitats demonstrate its feasibility for field studies. </span></li> <li><span>Diverse species interactions are based on sound, and our new tool can aid researchers studying acoustical signalling in predator-prey, pollinator-plant and mimic-model complexes, among others.</span></li> </ol>
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18
Audio and 3D flight-track recordings of mosquito responses to opposite-sex sound-stimuli
Open the record for dataset details and reuse information.
Data for: The buzzOmeter system: In situ audio recordings of pollinators in flight
Open the record for dataset details and reuse information.
Audio recordings of Atelpus varius calls from Panama
<p>Anurans (frogs and toads) are among the most globally threatened taxonomic groups. Successful conservation of anurans will rely on improved data on the status and changes in local populations, particularly for rare and threatened species. Automated sensors, such as acoustic recorders, have the potential to provide such data by massively increasing the spatial and temporal scale of population sampling efforts.</p> <p>We used AudioMoth autonomous recorders to survey for the critically endangered Harlequin toad (<em>Atelopus varius</em>) in Panama. This sampling effort generated thousands of hours of audio recordings from stream-side transects. <em>Atelopus varius </em>has a distinctive call with fast amplitude modulation of about 120 pulses per second. We used the opens-source Repeat Interval-Based Bioacoustic Identification Tool (RIBBIT), which classifies anuran vocalizations in audio recordings based on their periodic structure, to detect <em>A. varius </em>vocalizations in the data. Here we provide 70 detected recordings of <em>A. varius</em>. These recordings represent a dramatic increase in the total number of openly available audio recordings of <em>A. varius</em> vocalizations. </p>
ScienceDex guides
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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.