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24 results for “acoustic identification”
InsectSet32: Dataset for automatic acoustic identification of insects (Orthoptera and Cicadidae)
<p>This dataset contains recordings of 32 sound producing insect species with a total 335 files and a length of 57 minutes. The dataset was compiled for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was <a href="https://doi.org/10.1371/journal.pcbi.1011541">published</a> in PLOS Computational Biology and this dataset can be used to replicate the results, as well as other uses. The scripts for audio processing and the machine learning implementations are published on <a href="https://github.com/mariusfaiss/InsectSet32-Adaptive-Representations-of-Sound-for-Automatic-Insect-Recognition">Github</a>.</p> <p>The recordings are split into two datasets. Roughly half of the recordings (147) are of nine species belonging to the order Orthoptera. These recordings stem from a dataset that was originally compiled by <a href="https://orcid.org/0000-0002-8929-2737">Baudewijn Odé</a> (unpublished). </p> <p>The remaining recordings (188) are of 23 species in the family Cicadidae. These recordings were selected from the Global Cicada Sound Collection hosted on <a href="https://bio.acousti.ca/">Bioacoustica</a> (<a href="https://doi.org/10.1093/database/bav054">doi.org/10.1093/database/bav054</a>), including recordings published in <a href="https://doi.org/10.3897/BDJ.3.e5792">doi.org/10.3897/BDJ.3.e5792</a> & <a href="https://doi.org/10.11646/zootaxa.4340.1">doi.org/10.11646/zootaxa.4340.1</a>. Many recordings from this collection included speech annotations in the beginning of the recordings, therefore the last ten seconds of audio were extracted and used in this dataset. </p> <p>All files were manually inspected and files with strong noise interference or with sounds of multiple species were removed. Between species, the number of files ranges from four to 22 files and the length from 40 seconds to almost nine minutes of audio material for a single species. The files range in length from less than one second to several minutes. All original files were available with sample rates of at least 44.1 kHz or higher but were resampled to 44.1 kHz mono WAV files for consistency. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation).</p>
InsectSet47 & InsectSet66: Expanded datasets for automatic acoustic identification of insects (Orthoptera and Cicadidae)
<p><strong>Updated full version with training, validation and test sets.</strong></p> <p>Two newly compiled datasets for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was <a href="https://doi.org/10.1371/journal.pcbi.1011541">published in PLOS</a> Computational Biology and the machine learning implementations were published on <a href="https://github.com/mariusfaiss/InsectSet47-InsectSet66-Adaptive-Representations-of-Sound-for-Automatic-Insect-Recognition">Github</a>.</p> <p>These datasets expand on the previously published <a href="https://doi.org/10.5281/zenodo.7072196">InsectSet32</a> by including recently published collections of insect recordings by citizen scientists from around the world. Recordings from <a href="https://bio.acousti.ca/">BioAcoustica</a>, <a href="http://xeno-canto.org/">xeno-canto</a> and <a href="http://inaturalist.org/">iNaturalist</a>, as well as private collections by <a href="https://orcid.org/0000-0002-8929-2737">Baudewijn Odé</a> were downloaded and manually inspected. Files with strong noise interference or intense filtering, as well as files containing sounds of multiple species were removed to compile these datasets. The files were standardised to 44.1 kHz mono WAV files ranging in length from less than one second to several minutes. Files containing long periods without insect sounds were edited into multiple smaller files with silent periods no longer than 5 seconds. These files are marked as edits in the annotation file and should be assigned together into train/validation/test sets to prevent data leakage. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation).</p> <p>InsectSet47 expands on <a href="https://doi.org/10.5281/zenodo.7072196">InsectSet32</a> with recordings from <a href="http://xeno-canto.org/">xeno-canto</a> and contains 1006 original recordings from 47 species, with at least ten files per species. The total length of InsectSet47 is 22 hours. InsectSet66 further expands on InsectSet47 by adding research-grade audio observations from <a href="http://inaturalist.org/">iNaturalist</a>, with a total of 1554 recordings from 66 species, a total length of over 24 hours and a minimum of ten files per species.</p> <p>The datasets were split into the training, validation and test sets while ensuring a roughly equal distribution of audio files and audio material for every species in all three subsets. This resulted in a 60/20/20 split (train/validation/test) by file number and a 64/19.5/16.5 split by file length.</p>
Datasets for automatic acoustic identification of individual birds
<p>Bird individual audio recordings (foreground and background) to accompany the work:</p> <p><em><strong>"Automatic acoustic identification of individuals: Improving generalisation across species and recording conditions"</strong></em><br> by Dan Stowell, Tereza Petrusková, Martin Šálek, Pavel Linhart</p> <p><a href="https://royalsocietypublishing.org/doi/10.1098/rsif.2018.0940">https://royalsocietypublishing.org/doi/10.1098/rsif.2018.0940</a></p> <p><br> This dataset contains labelled recordings of individuals from three different bird species:</p> <ul> <li>Little owl</li> <li>Chiffchaff</li> <li>Tree Pipit</li> </ul> <p>For more information, please see the README.txt file, and the research article.</p> <p>The dataset takes approx 11 GB of disk space after the ZIP files have been uncompressed.</p> <p> </p>
Caller identification and characterization of individual humpback whale acoustic behavior
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FIG. 2 in Acoustic identification of bats in the southern Western Ghats, India
FIG. 2. Echolocation calls of bats with a strong CF component
Systematic literature review of acoustic individual identification (AIID)
<p>Citations and characteristics of manuscripts considered relevant to acoustic individual identification (AIID). To be included in the review, at minimum a paper was required to: 1) Use recorded vocalizations, regardless of the method, and 2) attempt to differentiate individuals by vocal signature as an objective or as a step towards more advanced application of acoustic IID. Detailed methods are available in the "AIIDLiteratureReviewMethods.docx".</p>
Identification of Ionospheric Acoustic Wave Signatures from Conventional Surface Explosions Using MF/HF Doppler Sounding
<p>These HDF5 files contain complex time series data from HF receptions of a Digisonde Portable Sounder 4D (DPS4D). Each data point is the phase and amplitude of a decoded Sky Map mode pulse. </p>
The soundscape of swarming: Proof of concept for a non-invasive acoustic species identification of swarming Myotis bats
<p>Bats emit echolocation calls to orientate in their predominantly dark environment. Recording of species-specific calls can facilitate species identification, especially when mist-netting is not feasible. However, some taxa, such as Myotis bats are hard to distinguish acoustically. In crowded situations where calls of many individuals overlap the subtle differences between species are additionally attenuated. Here we sought to non-invasively study the phenology of <em>Myotis</em> bats during autumn swarming at a prominent hibernaculum. To do so we recorded sequences of overlapping echolocation calls (N=564) during nights of high swarming activity and extracted spectral parameters (peak frequency, start frequency, spectral centroid) and Linear Frequency Cepstral Coefficients (LFCCs) which additionally encompass the timbre (vocal 'colour') of calls. We used this parameter combination in a stepwise discriminant function analysis (DFA) to classify the call sequences to species level. A set of previously identified call sequences of single flying <em>Myotis</em> <em>daubentonii</em> and <em>Myotis</em> <em>nattereri</em>, the most common species at our study site, functioned as a training set for the DFA. 90.2% of the call sequences could be assigned to either <em>M</em>. <em>daubentonii</em> or <em>M</em>. <em>nattereri</em>, indicating the predominantly swarming species at the time of recording. We verified our results by correctly classifying a second set of previously identified call sequences with an accuracy of 100%. In addition, our acoustic species classification corresponds well to the existing knowledge on swarming phenology at the hibernaculum. Moreover, we successfully classified call sequences from a different hibernaculum to species level and verified our classification results by capturing swarming bats while we recorded them. Our findings provide the basis for a new non-invasive acoustic monitoring technique that analyses "swarming soundscapes" by combining classical acoustic parameters and LFCCs, instead of analysing single calls. Our approach for species identification is especially beneficial in situations with multiple calling individuals, such as autumn swarming.</p>
Identification of transient seismo-acoustic signals from crashing ocean waves: Template matching and location of discrete surf events
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Statistical code from: Passive acoustic monitoring with AI-based detection and identification reveal sooty grouse hooting patterns in western Oregon
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The soundscape of swarming: Proof of concept for a non-invasive acoustic species identification of swarming Myotis bats
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Data from: Acoustic identification of Mexican bats based on taxonomic and ecological constraints on call design
Monitoring global biodiversity is critical for understanding responses to anthropogenic change, but biodiversity monitoring is often biased away from tropical, megadiverse areas that are experiencing more rapid environmental change. Acoustic surveys are increasingly used to monitor biodiversity change, especially for bats as they are important indicator species and most use sound to detect, localise and classify objects. However, using bat acoustic surveys for monitoring poses several challenges, particularly in megadiverse regions. Many species lack reference recordings, some species have high call similarity or differ in call detectability, and quantitative classification tools, such as machine learning algorithms, have rarely been applied to data from these areas. Here, we collate a reference call library for bat species that occur in a megadiverse country, Mexico. We use 4685 search-phase calls from 1378 individual sequences of 59 bat species to create automatic species identification tools generated by machine learning algorithms (Random Forest). We evaluate the improvement in species-level classification rates gained by using hierarchical classifications, reflecting either taxonomic or ecological constraints (guilds) on call design, and examine how classification rate accuracy changes at different hierarchical levels (family, genus and guild). Species-level classification of calls had a mean accuracy of 66%, and the use of hierarchies improved mean species-level classification accuracy by up to 6% (species within families 72%, species within genera 71·2% and species within guilds 69·1%). Classification accuracy to family, genus and guild-level was 91·7%, 77·8% and 82·5%, respectively. The bioacoustic identification tools we have developed are accurate for rapid biodiversity assessments in a megadiverse region and can also be used effectively to classify species at broader taxonomic or ecological levels. This flexibility increases their usefulness when there are incomplete species reference recordings and also offers the opportunity to characterise and track changes in bat community structure. Our results show that bat bioacoustic surveys in megadiverse countries have more potential than previously thought to monitor biodiversity changes and can be used to direct further developments of bioacoustic monitoring programs in Mexico.
FIGURE 3 in Acoustic and morphological identification of the sympatric cricket frogs Acris crepitans and A. gryllus and the disappearance of A. gryllus near the edge of its range
FIGURE 3. Morphological measurements on a preserved A. gryllus specimen. A. Measurements of larger features and locations of smaller features. B. Measurements of thigh stripe length and location of the right anal tubercle. C. Ventral view of hind foot pinned to extend webbing. Arrows indicate phalangeal joints, which visually demarcate each phalange.
FIGURE 2 in Acoustic and morphological identification of the sympatric cricket frogs Acris crepitans and A. gryllus and the disappearance of A. gryllus near the edge of its range
FIGURE 2. Sites at which Acris were found in 2004–2008. Sites are numbered according to when Acris were first observed (1–17 in 2004; 18–32 in 2005; 33–36 in 2007; 37–74 in 2008). Sites surveyed in 2008 included 11 where Bartlett collected in 1962–1963.
FIGURE 4 in Acoustic and morphological identification of the sympatric cricket frogs Acris crepitans and A. gryllus and the disappearance of A. gryllus near the edge of its range
FIGURE 4. Sites where Acris collected by Peter N. Bartlett in 1962–1963 were identified using a discriminant function. Sites are numbered by the authors. The numeration includes sites that were not assessed using the discriminant function.
Data from: Acoustic identification of Mexican bats based on taxonomic and ecological constraints on call design
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Animal acoustic identification, denoising, and source separation using generative adversarial networks
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FIG. 5 in Acoustic identification of bats in the southern Western Ghats, India
FIG. 5. Biogeographic variation in the FMAXE of CF bats: Valparai, Tamil Nadu, India (this paper); Yercaud, Tamil Nadu, India, n = 38 (Chattopadhyay et al., 2012); Sirumalai, Tamil Nadu, India, n = 2 (Chattopadhyay et al., 2012); Meghamala, Tamil Nadu, India, n = 12 (Chattopadhyay et al., 2012); Srirangapattana, Karnataka, India, n = 13 (Chattopadhyay et al., 2012); Moodbidri, Karnataka, India, n = 32 (Chattopadhyay et al., 2012); Mahabaleswar, Maharashtra, India (Schuller, 1980); Sri Lanka 1 (Neuweiler et al., 1987); Sri Lanka 2, n = 16 (Behrend and Schuller, 2000); Sri Lanka 3, n = 7 (Kössl, 1994); Hainan, China (Zhang et al., 2009); Yunnan, China (Shi et al., 2009; Zhang et al., 2009); Guangdong, China (Zhang et al., 2009); Hong Kong (Shek and Lau, 2006); Lao PDR (Francis and Habersetzer, 1998; Francis, 2008); Northern Vietnam, n = 7 (Furey et al., 2009); Myanmar 1 (Streubig et al., 2005); Myanmar 2 (Douangboubpha et al., 2010); Central Thailand (Douangboubpha et al., 2010); Central and North Thailand, n = 36 (Douangboubpha et al., 2010); Thailand 1, H. pomona — n = 85, R. lepidus — n = 69 (Hughes et al., 2010, 2011); Thailand 2, n = 1 (Soisook et al., 2010); Singapore, n = 4 (Pottie et al., 2005). n refers to the number of individual bats recorded in the studies cited
FIG. 1 in Acoustic identification of bats in the southern Western Ghats, India
FIG. 1. The location of the Valparai plateau in the southern Western Ghats. The inset shows the location of the southern Western Ghats in the Indian subcontinent
FIGURE 1 in Acoustic and morphological identification of the sympatric cricket frogs Acris crepitans and A. gryllus and the disappearance of A. gryllus near the edge of its range
FIGURE 1. Representative clicks of Acris crepitans and A. gryllus in North Carolina.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.