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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.
DCASE 2024 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description:</strong></p> <p>The development set for task 5 of DCASE 2024 "Few-shot Bioacoustic Event Detection" consists of 217 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p><strong>Folder Structure:</strong></p> <p><em>Development_set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___WMW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p> |__Validation_Set/</p> <p> |___HB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB24/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___RD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_set_annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br>TRAINING SET<br>-----------------------------------------------------<br>Number of audio recordings | 174<br>Total duration | 21 hours<br>Total classes | 47<br>Total events | 14229<br>-----------------------------------------------------<br>TRAINING SET/BV<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 10 hours<br>Total classes | 11<br>Total events | 9026<br>Sampling rate | 24000 Hz<br>-----------------------------------------------------<br>TRAINING SET/HT<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 5 hours<br>Total classes | 5<br>Total events | 611<br>Sampling rate | 6000 Hz<br>-----------------------------------------------------<br>TRAINING SET/JD<br>-----------------------------------------------------<br>Number of audio recordings | 1<br>Total duration | 10 mins<br>Total classes | 1<br>Total events | 357<br>Sampling rate | 22050 Hz<br>-----------------------------------------------------<br>TRAINING SET/MT<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 1 hour and 10 mins<br>Total classes | 4<br>Total events | 1294<br>Sampling rate | 8000 Hz<br>-----------------------------------------------------<br>TRAINING SET/WMW<br>-----------------------------------------------------<br>Number of audio recordings | 161<br>Total duration | 4 hours and 40 mins<br>Total classes | 26<br>Total events | 2941<br>Sampling rate | various sampling rates<br>-----------------------------------------------------</p> <p>-----------------------------------------------------<br>VALIDATION SET<br>-----------------------------------------------------<br>Number of audio recordings | 43<br>Total duration | 49 hours and 57 minutes<br>Total classes | 7<br>Total events | 3504<br>-----------------------------------------------------<br>VALIDATION SET/HB<br>-----------------------------------------------------<br>Number of audio recordings | 10<br>Total duration | 2 hours and 38 minutes<br>Total classes | 1<br>Total events | 712<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 3 hours<br>Total classes | 2<br>Total events | 292<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/ME<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 20 minutes<br>Total classes | 2<br>Total events | 73<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB24<br>-----------------------------------------------------<br>Number of audio recordings | 4<br>Total duration | 2 hours<br>Total classes | 2<br>Total events | 350<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/RD<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 18 hours<br>Total classes | 1<br>Total events | 1372<br>Sampling rate | 48000 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PW<br>-----------------------------------------------------<br>Number of audio recordings | 15<br>Total duration | 24 hours<br>Total classes | 1<br>Total events | 705<br>Sampling rate | 96000 Hz<br>-----------------------------------------------------</p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br>---------------------<br>Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br>---------------------<br>Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2024_task5_training_set_classes.csv and DCASE2024_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set. Additionally, DCASE2024_task5_validation_set_classes.csv also provides a recording names column.</p> <p>DCASE2024_task5_training_set_classes.csv<br>---------------------<br>dataset, class_code, class_name</p> <p>DCASE2024_task5_validation_set_classes.csv<br>---------------------<br>dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for this task will be released on the 1 June 2024</p> <p><strong>Open Access:</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info:</strong></p> <p>Please send any feedback or questions to:</p> <p>Burooj Ghani - burooj.ghani@naturalis.nl | Ines Nolasco - i.dealmeidanolasco@qmul.ac.uk</p> <p>Alternately, join us on Slack: <a href="https://join.slack.com/t/dcase/shared_invite/zt-12zfa5kw0-dD41gVaPU3EZTCAw1mHTCA">task-fewshot-bio-sed</a></p> <p> </p>
DCASE 2021 Task 5: Few-shot Bioacoustic Event Detection Evaluation Set
<p><strong>General Description</strong></p> <p>The evaluation set for task 5 of DCASE 2021 "Few-shot Bioacoustic Event Detection" consists of 31 audio files acquired from different bioacoustic sources. </p> <p>In Evaluation_Set_Annotations: the first 5 annotations are provided for each file, with events marked as positive (POS) for the class of interest. This is the same setup used during the DCASE 2021 challenge.</p> <p>In Evaluation_Set_Full_Annotations: the full annotations are provided for each file, with events marked as positive (POS) or unknown (UNK) for the class of interest.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p><em>Evaluation_Set.zip contains audio files and annotation files with 5 first POS events (as used during DCASE 2021 challenge)</em></p> <p>|__Evaluation_Set/</p> <p> |___DC/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ML/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p><em>Evaluation_Set_Audio.zip</em> has the same structure but contains only the *.wav files.</p> <p><em>Evaluation_Set_Annotations.zip</em> has the same structure but contains only the *.csv files with first 5 POS annotations.</p> <p><em>Evaluation_Set_Full_Annotations.zip</em> has the same structure but contains only the *.csv files with all POS annotations.</p> <p>The subfolders denote different recording sources and there may or may not be overlap between classes of interest from different wav files.</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:<br> [ Audiofilename, Starttime, Endtime, Q ]</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2021_task5_evaluation_set.csv provides a table with class code correspondance to class name for all the recordings of the Evaluation set.</p> <p>DCASE2021_task5_evaluation_set.csv<br> -------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Development Set</strong></p> <p>The development set for the same task can be found at: <a href="https://doi.org/10.5281/zenodo.5412896">https://doi.org/10.5281/zenodo.5412896</a></p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info</strong></p> <p>Please send any feedback or questions to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk</p>
DCASE 2021 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description</strong></p> <p>The development set for task 5 of DCASE 2021 "Few-shot Bioacoustic Event Detection" consists of 19 audio files acquired from different bioacoustic sources. The dataset is split into training and validation Sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p> </p> <p><strong>Folder Structure</strong></p> <p><em>Development_Set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |__Validation_Set/</p> <p> |___HV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_Set_Audio.zip</em> has the same structure but contains only the *.wav files.</p> <p><em>Development_Set_Annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br> TRAINING SET<br> -----------------------------------------------------<br> Number of audio recordings | 11<br> Total duration | 14 hours and 20 mins<br> Total classes (excl. UNK) | 19<br> Total events (excl. UNK) | 4,686<br> -----------------------------------------------------<br> TRAINING SET/BV<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 10 hours<br> Total classes (excl. UNK) | 11<br> Total events (excl. UNK) | 2,662<br> Sampling rate | 24,000 Hz<br> -----------------------------------------------------<br> TRAINING SET/HT<br> -----------------------------------------------------<br> Number of audio recordings | 3<br> Total duration | 3 hours<br> Total classes (excl. UNK) | 3<br> Total events (excl. UNK) | 435<br> Sampling rate | 6,000 Hz<br> -----------------------------------------------------<br> TRAINING SET/JD<br> -----------------------------------------------------<br> Number of audio recordings | 1<br> Total duration | 10 mins<br> Total classes (excl. UNK) | 1<br> Total events (excl. UNK) | 355<br> Sampling rate | 22,050 Hz<br> -----------------------------------------------------<br> TRAINING SET/MT<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 1 hour and 10 mins<br> Total classes (excl. UNK) | 4<br> Total events (excl. UNK) | 1,234<br> Sampling rate | 8,000 Hz<br> -----------------------------------------------------</p> <p><br> -----------------------------------------------------<br> VALIDATION SET<br> -----------------------------------------------------<br> Number of audio recordings | 8<br> Total duration | 5 hours<br> Total classes (excl. UNK) | 4<br> Total events (excl. UNK) | 310<br> -----------------------------------------------------<br> VALIDATION SET/HV<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 2 hours<br> Total classes (excl. UNK) | 2<br> Total events (excl. UNK) | 50<br> Sampling rate | 6,000 Hz<br> -----------------------------------------------------<br> VALIDATION SET/PB<br> -----------------------------------------------------<br> Number of audio recordings | 6<br> Total duration | 3 hours<br> Total classes (excl. UNK) | 2<br> Total events (excl. UNK) | 260<br> Sampling rate | 44,100 Hz<br> -----------------------------------------------------</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2021_task5_training_set_classes.csv and DCASE2021_task5_validation_set_classes.csv provide a table with class code correspondace to class name for all classes in the Development set.</p> <p>DCASE2021_task5_training_set_classes.csv<br> ---------------------<br> dataset, class_code, class_name</p> <p>DCASE2021_task5_validation_set_classes.csv<br> ---------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for the same task can be found at: <a href="https://doi.org/10.5281/zenodo.5413149">https://doi.org/10.5281/zenodo.5413149</a></p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><br> <strong>Contact info</strong></p> <p>Please send any feedback or questions to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk<br> </p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
FIGURE 3 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 3 Schematic drawing of the ear of Gadus morhua (anterior is to the left): (a) top view of the body showing the location of the ears in the cranial cavity as well as the proximity of the rostral end of the swim bladder to the ear; (b) lateral and (c) top view of the same ear. Each ear is set at an angle relative to the midline of the fish., The otolith organs,, the semicircular canals (enlarged areas are the ampullae regions that contain the sensory cells);, the dense calcarious otolith lying in close proximity to the sensory epithelium (). Also see Figure 4. Fig. © 2018 Anthony D. Hawkins, all rights reserved
FIGURE 5 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 5 The sensory epithelia of the end organs of the inner ear have numerous mechanoreceptive sensory hair cells. The apical ends of these cells, directed into the lumen of the epithelia, have ciliary bundles (inserts in the figure) consisting of a single kinocilium (longest of the cilia) and graded stereocilia. Bending of the ciliary bundle during sound stimulation results in neurotransmitter release to stimulate the 8th cranial nerve. The sensory cells on the otolith maculae are organized into orientation groups, with all of the cells in each group having their kinocilia in the same general direction. In this typical saccular epithelium (anterior to the left, dorsal to the top), the cilia on the rostral end are oriented rostrally or caudally, while the cells on the caudal end are oriented dorsally and ventrally., The approximate dividing lines between orientation groups)
FIGURE 2 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 2 Masking in the Gadus morhua and Salmo salar by ambient noise. The thresholds were determined using a pure tone signal at a frequency of 160 Hz. The ambient noise (natural sea noise, augmented by white noise from a loudspeaker) is expressed as the spectrum level at that same frequency (dB re 1 μPa/Hz). Closed symbols, thresholds to natural levels of ambient noise; open symbols, thresholds to anthropogenic noise. n.b., The thresholds in S. salar were only influenced by high noise levels, above the natural ambient levels of noise (data from Hawkins, 1993). Fig. © 2018 Anthony D. Hawkins, all rights reserved
FIGURE 4 A in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 4 A frontal view of the head of Gadus morhua showing a section of the saccule (). The saccular chamber is filled with perilymph and contains the otolith (), which lies close to the sensory hair cells of the epithelium (macula). The hair cells are innervated by the eighth cranial nerve. Fig. © 2018 Anthony D. Hawkins, all rights reserved
FIGURE 1 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 1 Fish hearing sensitivity (thresholds) obtained under open sea, free-field, conditions in response to pure tone stimuli at different frequencies. The lower the thresholds (y-axis), the more sensitive the fish is to a sound. Thus, Clupea harengus has best hearing of all of these species over a wider range of frequencies. Note that the thresholds in Gadus morhua and C. harengus obtained under quiet conditions may be below natural ambient noise levels, especially at their most sensitive frequencies. In the presence of higher levels of noise, the thresholds would be raised, a phenomenon referred to as masking. Gadus morhua and C. harengus are sensitive to both sound pressure and particle motion, whereas Limanda limanda and Salmo salar are only sensitive to particle motion. The reference level for the particle velocity is based on the level that exists in a free sound field for the given sound pressure level. n.b., For the particle velocity levels in this figure to match the sound pressure levels in a free sound field it is necessary to calculate an appropriate particle velocity reference level. If the standard reference levels are used, then the curves will not match one another and so they are not included here to keep the figure relatively simple. Fig. © 2018 Anthony D. Hawkins, all rights reserved
Data from: Genomic and bioacoustic variation in a midwife toad hybrid zone: a role for reinforcement?
<p>This data package includes the following datasets and scripts used in the corresponding publication: </p> <ul> <li>An alignment (fasta format) of the 16S sequences obtained fromt the 221 new <em>A. obstetricans</em>/<em>almogavarii </em>samples barcoded in this study + the <em>A. cisternasii</em> sequence used as outgroup (16S_alignment.fas).</li> <li>A matrix of 1,642 SNPs genotyped in 89 <em>A. obstetricans</em>/<em>almogavarii </em>samples used for ancestry analyses (n89p41r0.5wrs_1642SNP_STRUCTURE.str).</li> <li>The R script used to compute geographic clines with HZAR and their graphical displays (Cline_analyses.r) and the input files it uses (Transect_Q_mtDNA_HZAR.csv; Transect_n57p19r0.5_diag_loci_HZAR.csv; dist_transect.txt; clines_diag_SNPs_1perlocus.csv).</li> <li>The R script used for the bioacoustic analyses (Alytes_FR_Bioacoustics.r) and the corresponding input data extracted from 71 mating calls of <em>A. obstetricans</em>/<em>almogavarii </em>(Alytes_FR_Bioacoustics.csv) the R script used for their analysis.</li> </ul>
DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description:</strong></p> <p>The development set for task 5 of DCASE 2022 "Few-shot Bioacoustic Event Detection" consists of 192 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p><strong>this version (3):</strong><br> * fixes issues with annotations from HB set</p> <p> </p> <p><strong>Folder Structure:</strong></p> <p><em>Development_Set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___WMW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p> |__Validation_Set/</p> <p> |___HB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_Set_Annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>## Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br> TRAINING SET<br> -----------------------------------------------------<br> Number of audio recordings | 174<br> Total duration | 21 hours<br> Total classes | 47<br> Total events | 14229<br> -----------------------------------------------------<br> TRAINING SET/BV<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 10 hours<br> Total classes | 11<br> Total events | 9026<br> Ratio event/duration | 0.04<br> Sampling rate | 24000 Hz<br> -----------------------------------------------------<br> TRAINING SET/HT<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 5 hours<br> Total classes | 5<br> Total events | 611<br> Ratio event/duration | 0.05<br> Sampling rate | 6000 Hz<br> -----------------------------------------------------<br> TRAINING SET/JD<br> -----------------------------------------------------<br> Number of audio recordings | 1<br> Total duration | 10 mins<br> Total classes | 1<br> Total events | 357<br> Ratio event/duration | 0.06<br> Sampling rate | 22050 Hz<br> -----------------------------------------------------<br> TRAINING SET/MT<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 1 hour and 10 mins<br> Total classes | 4<br> Total events | 1294<br> Ratio event/duration | 0.04<br> Sampling rate | 8000 Hz<br> -----------------------------------------------------<br> TRAINING SET/WMW<br> -----------------------------------------------------<br> Number of audio recordings | 161<br> Total duration | 4 hours and 40 mins<br> Total classes | 26<br> Total events | 2941<br> Ratio event/duration | 0.24<br> Sampling rate | various sampling rates<br> -----------------------------------------------------</p> <p>-----------------------------------------------------<br> VALIDATION SET<br> -----------------------------------------------------<br> Number of audio recordings | 18<br> Total duration | 5 hours and 57 minutes<br> Total classes | 5<br> Total events | 1077<br> -----------------------------------------------------<br> VALIDATION SET/HB<br> -----------------------------------------------------<br> Number of audio recordings | 10<br> Total duration | 2 hours and 38 minutes<br> Total classes | 1<br> Total events | 712<br> Ratio event/duration | 0.7<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------<br> VALIDATION SET/PB<br> -----------------------------------------------------<br> Number of audio recordings | 6<br> Total duration | 3 hours<br> Total classes | 2<br> Total events | 292<br> Ratio event/duration | 0.003<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------<br> VALIDATION SET/ME<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 20 minutes<br> Total classes | 2<br> Total events | 73<br> Ratio event/duration | 0.01<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2022_task5_training_set_classes.csv and DCASE2022_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set.</p> <p>DCASE2022_task5_training_set_classes.csv<br> ---------------------<br> dataset, class_code, class_name</p> <p>DCASE2022_task5_validation_set_classes.csv<br> ---------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for this task will be released on the 1st of June 2022</p> <p><strong>Open Access:</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info:</strong></p> <p>Please send any feedback or questions to:</p> <p>Ines Nolasco - i.dealmeidanolasco@qmul.ac.uk</p>
Fig. 2 in Bioacoustics of Acanthoscelides obtectus (Coleoptera: Chrysomelidae: Bruchinae) on Phaseolus vulgaris (Fabaceae)
Fig. 2. Oscillograms and spectrograms of signals recorded from beans infested with A) adults and B) larvae of Acanthoscelides obtectus. Darker shade in spectro- gram indicates greater energy at specified frequency and time.
Fig. 1 in Bioacoustics of Acanthoscelides obtectus (Coleoptera: Chrysomelidae: Bruchinae) on Phaseolus vulgaris (Fabaceae)
Fig. 1. Total counts of impulses of each profile type detected in recordings from the larvae and adults of Acanthoscelides obtectus.
Fig. 3 in Bioacoustics of Acanthoscelides obtectus (Coleoptera: Chrysomelidae: Bruchinae) on Phaseolus vulgaris (Fabaceae)
Fig. 3. Oscillograms of a 1 s period of signals recorded from beans infested with A) adults and B) larvae of Acanthoscelides obtectus. Signals enclosed by a dashed oval indicate bursts of the adults (a and b) and larvae (c and d).
Figures 4a-4c in Bioacoustic analysis of a compound sound with stridulation and forced air produced by the larva of Phileurus valgus (Olivier, 1789) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 4a-4c. Oscillograms (above) and spectrograms (down) of sounds emitted by Phileurus valgus larvae. a) Bioacoustic stridulation (isolated) patterns. b) Bioacoustic sound patterns of compound sound (stridulatory + forced air). c) Bioacoustic pattern of compound sound pulse and some forced air sound pulses (6 pulses after the compound sound). / Oscilogramas (arriba) y espectrogramas (abajo) de los sonidos emitidos por las larvas de Phileurus valgus. a) Patrones bioacústicos de estridulación (aislada). b) Patrones bioacústicos de sonido compuesto (estridulación + aire forzado). c) Patrón bioacústico de pulso de sonido compuesto y algunos pulsos de sonido de aire forzado (6 pulsos después del sonido compuesto).
Figures 3a-3b in Bioacoustic analysis of a compound sound with stridulation and forced air produced by the larva of Phileurus valgus (Olivier, 1789) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 3a-3b. Comparison of oscillogram and spectrogram of forced air sound pulse. a. Phileurus valgus. b. Phileurus didymus. / Comparación del oscilograma y el espectrograma del pulso sonoro de aire forzado. a. Phileurus valgus. b. Phileurus didymus.
Figures 1a-1d in Bioacoustic analysis of a compound sound with stridulation and forced air produced by the larva of Phileurus valgus (Olivier, 1789) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 1a-1d. Diagrams of the experiments. a-c) Direct interaction (larva-larva) and response experiment. d) Direct manipulation experiment. / Diagramas de los experimentos. a-c) Experimento de interacción directa (larva-larva) y respuesta. d) Experimento de manipulación directa.
Figures 2a-2c in Bioacoustic analysis of a compound sound with stridulation and forced air produced by the larva of Phileurus valgus (Olivier, 1789) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 2a-2c. Comparison of numbers of pulses in stridulation, oscillogram (above). Spectrogram (down). a) 4 pulses. b) 10 pulses. c) 12 pulses. / Comparación del número de pulsos en la estridulación, oscilograma (arriba). Espectrograma (abajo). a) 4 pulsos. b) 10 pulsos. c) 12 pulsos.
Figures 4a, b in Bioacoustic study of a sound produced with forced air by larvae of Phileurus didymus (Linnaeus) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 4a, b. Comparison of oscillograms. (a) Stridulatory sound (stridulatory teeth) of a Dynastinae beetle larvae. (b) Forced air sounds of P. didymus.
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.