Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15
datasets available to search
ShareScore release 0.9.0
Dataset results
15 results for “event classification”
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
<p>The problem of training a deep neural network with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show very good performance when trained with large datasets. However, in many applications, it is not possible to obtain such a high number of samples. In the image domain, typical FSL applications are those related to face recognition. In the audio domain, music fraud or speaker recognition can be clearly benefited from FSL methods. This paper deals with the application of FSL to the detection of specific and intentional acoustic events given by different types of sound alarms, such as door bells or fire alarms, using a limited number of samples. These sounds typically occur in domestic environments where many events corresponding to a wide variety of sound classes take place. Therefore, the detection of such alarms in a practical scenario can be considered an open-set recognition (OSR) problem. To address the lack of a dedicated public dataset for audio FSL, researchers usually make modifications on other available datasets. This paper is aimed at providing the audio recognition community with a carefully annotated dataset for FSL and OSR comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds. To facilitate and promote research in this area, results with two baseline systems (one trained from scratch and another based on transfer learning), are presented.</p> <p> </p>
Dataset-AOB: urban sounds events classification
<p>The dataset Dataset-AOB is an audio dataset collected and manually edited for urban sounds events classification using Convolutional Neural Networks for the Master Thesis: </p> <p>Ospina, A. "Audio Event Classification using Deep Learning. Use case: Urban Sounds Events classification with Convolutional Neural Networks," M.Eng. thesis, Beuth University of Applied Sciences, Berlin, 2020.</p> <p>- 10 audio events: alarm-siren, children playing, dog bark, engine, footsteps, glass breaking, gun shot, metro train, rain and screams.</p> <p>- duration: < 4 seconds</p> <p>- format: (.wav)</p> <p>- sampling rate: 22KHz - 44KHz</p> <p>- files: Dataset-AOB: development dataset (4831 samples), DatasetEVAL-AOB: evaluation (218 samples)</p> <p>- metadata: (.csv)</p> <p>- sources per class: (.png)</p> <p>Contact: aospinab@gmail.com</p>
Data Regarding Classification of Infrasonic Atmospheric Events Using Electromagnetic Pulse Analysis
<p> </p> <div>The following data files were used for the analysis presented in the paper </div> <div>"Classification of Infrasonic Atmospheric Events Using Electromagnetic Pulse Analysis"</div> <div> </div> <div>The files include details of the infrasonicly detected evnents, and features extracted from electromagnetic signals, as explined in the README file.</div>
Real-Life Indoor Sound Event Dataset (ReaLISED) for Sound Event Classification (SEC)
<p>The Real-Life Indoor Sound Event Dataset (ReaLISED) offers the scientific community the possibility of testing Sound Event Classification (SEC) algorithms with new real indoor audio event recordings. The full set is made up of 2479 sound recordings of 18 events. The 18 event classes are the following: beater, cooking, cupboard/wardrobe, dishwasher, door, drawer, furniture movement, microwave, object falling, smoke extractor, speech, switch, television, vacuum cleaner, walking, washing machine, water tap, and window. There are 2479 clips of isolated sounds, which result in 3624.51 seconds. The number of events in each class is between 104 for the "Window" class and 190 for the “Speech” class, with a mean value of 138 events and a standard deviation of 25.</p> <p>Four Olympus LS-100 recorders were used. The sampling frequency was set to 44.1 kHz and 24 bits per sample. The stereo mode was used, and a medium sensitivity of the microphone was set. The distance between the recorder and the sound source was set to approximately 30-40 cm.</p> <p>Apart from the labels related to the class of event, extra information for each recording is provided in order to be exploited if necessary in the future, with other research purposes. This extra information completes the description of the sound source.</p> <p>The dataset is introduced to the scientific community by providing all the .flac files which composed it. The name of the files is built with 5 pieces of information, separated with underscores (“_”), with the format “abc_123_45_67_8.flac′′:</p> <ul> <li> <p>“abc”: the first three letters indicate the source that produces the sound. This segment can take 18 different values: ‘bea’ (beater), ‘coo’ (cooking), ‘cup’ (cupboard/wardrobe), ‘dis’ (dishwasher), ‘doo’ (door), ‘dra’ (drawer), ‘fur’ (furniture movement), ‘mic’ (microwave), ‘obj’ (object falling), ‘smo’ (smoke extractor), ‘spe’ (speech), ‘swi’ (switch), ‘tel’ (television), ‘vac’ (vacuum cleaner), ‘wal’ (walking), ‘was’ (washing machine), ‘wat’ (water tap), win’ (window).</p> </li> <li> <p>“123”: this set of digits identifies the event among the number of events produced by the source identified with “abc”. This segment can take all the values between ‘001’ and ‘190’, which is the maximum number of events of a particular class we can find in the dataset (speech).</p> </li> <li> <p>“45”: this set of digits identifies the action that produce the sound. This segment can take 11 different values: ‘01’ (close), ‘02’ (open), ‘03’ (throw), ‘04’ (turn on), ‘05’ (turn off), ‘06’ (move), ‘07’ (plug), ‘08’ (unplug), ‘09’ (raise), ‘10’ (lower), and ‘00’ (there is no information about the action).</p> </li> <li> <p>“67”: this set of digits identifies the material the sound source is made of. This segment can take 14 different values: ’01’ (wood), ’02’ (glass), ’03’ (metal), ’04’ (plastic), ’05’ (ceramic), ’06’ (synthetic), ’07’ (cardboard), ’08’ (marble), ’09’ (floating platform), ’10' (platelet), ’11’ (wicker), ’12’ (carpet), ’13’ (medium-density fibreboard MDF), and ’00’ (there is no information about the material).</p> </li> <li> <p>“8”: the last digit gives approximate information about the intensity of the recorded sound. It can take 4 different values: ’1’ (low intensity), ’2’ (medium intensity), ’3’ (high intensity), ’0’ (there ir no information about the intensity).</p> <p>For clarity, some examples of audio file names with this code are shown hereunder:</p> </li> <li> <p>“doo_040_02_00_3.flac” is the name of the 40th file in the Door class, described as “opening a door of unknown material with high intensity”.</p> </li> <li> <p>“fur_058_06_01_2.flac” is the name of the 58th file in the furniture movement class, described as “moving a wooden furniture with medium intensity”.</p> </li> <li> <p>“vac_001_00_00_0.flac” is the name of the 1st audio file in the vacuum cleaner class, described as “using the vacuum cleaner, without information about the action, neither the material or the intensity”.</p> </li> </ul>
Data set from 'Sequential Feature Selection for Power System Event Classification Utilizing Wide-Area PMU Data'
<p>The increasing penetration of intermittent, nonsynchronous<br> generation has led to a reduction in total power<br> system inertia. Low inertia systems are more sensitive to sudden<br> changes, and more susceptible to secondary issues that can result<br> in large scale events. Due to the short time frames involved,<br> automatic methods for power system event detection and diagnosis<br> are required. Wide-area monitoring systems can provide<br> the data required to detect and diagnose events; however due to<br> the increasing quantity of data it is next to impossible for power<br> system operators to manually process raw data. The important<br> information is required to be extracted and presented to system<br> operators for real/near-time decision making and control. This<br> paper demonstrates an approach for the wide-area classification<br> of a number of power system events. A mixture of sequential<br> feature selection and linear discriminant analysis is adopted<br> to reduce the dimensionality of PMU data. Successful event<br> classification is obtained by employing quadratic discriminant<br> analysis on wide-area synchronized frequency, phase angle and<br> voltage measurements. The reliability of the proposed method is<br> evaluated using simulated case studies and benchmarked against<br> other classification methods.</p>
Physical Features for my study on Automatic Seismic Event Classification System in Pacific Northwest (Origin time - 50, +100)
<p>These features used the revised version of the feature extraction code. The revision involves changing the envelope filtering options and some minor modifications. </p>
Trained Random Forest Model for PNW Seismic Event Classification Trained on 150s waveforms (P-50, P+100), 50 Hz, and 1-10 Hz BP Filtered
<p>This dataset contains three trained random forest models named as following - </p> <ul> <li>P_10_100_F_1_10_50.joblib - This is a model trained on 110s long waveforms (origin time - 10, origin time +100) in case of earthquakes and explosions and (first arrival pick -10, first arrival pick + 100) in case of surface events, the waveforms are tapered using 10% cosine taper, bandpass filtered between 1-10 Hz using Butterworth four corner filter, normalized and resampled to 50 Hz. </li> <li>P_50_100_F_1_10_50.joblib </li> <li>P_10_30_F_1_15_50.joblib. </li> </ul> <p>And also the standard scaler parameters for each features that will be used to normalize them. </p>
Scintillation event list database produced for "Multiyear Detection, Classification and Hypothesis of Ionospheric Layer Causing GNSS Scintillation"
<table> <tbody> <tr> <td>Database produced for and associated with journal article https://doi.org/10.1029/2021RS007328</td> </tr> <tr> <td>This database contains scintillation events identified by the "Detect, Classify, Hypothesize" method as described in the journal article. </td> </tr> <tr> <td>Created by Datta-Barua, Seebany.</td> </tr> <tr> <td>Open Access License Creative Commons Attribution 4.0 International</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Sheets labeled "S*_yyyy_freq" contain a list of scintillation events based on data from the SAGA array.</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>For S* the abbreviations are:</td> <td>"SP": sigma phi index, i.e., phase scintillation events</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"S4": S4 index, i.e., amplitude scintillation events</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"SPS4": sigma phi and S4, i.e., both-phase-and-amplitude events</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>yyyy indicates the four-digit year, from 2014 through 2019</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>freq:</td> <td>"L1CA": refers to GPS L1 frequency 1575 MHz</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"L2CL": refers to GPS L2C frequency 1227 MHz</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>In each sheet "S*_yyyy_freq" the columns are:</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column A:</td> <td>Year (2014-2019)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column B:</td> <td>day of year (1-366)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column C:</td> <td>frequency (1 for L1, 2 for L2C)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column D:</td> <td>GPS satellite PRN (1-32)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column E:</td> <td>Scintillation start UT hour (0-23)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column F:</td> <td>Scintillation start UT minute (0-59)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column G:</td> <td>Scintillation end UT hour (0-23)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column H:</td> <td>Scintillation end UT minute (0-59)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column I:</td> <td>number of receivers operational (4-6)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column J:</td> <td>PFISR-based hypothesized layer (1-7, as described below)</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>1:</td> <td>E layer (majority peak height < 150 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>2:</td> <td>F layer (majority peak height > 195 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>3:</td> <td>T layer (150 < majority peak height < 195 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4:</td> <td>I layer (no majority layer)</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>5:</td> <td>"N"- no Poker Flat Incoherent Scatter Radar (PFISR) long pulse data and no alternating code data</td> </tr> <tr> <td> </td> <td> </td> <td>6:</td> <td>"NLP" - no PFISR long pulse</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>7:</td> <td>"NAC" - no PFISR alternating code</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>A blank sheet indicates that there were no events of that type and at that frequency found in that year.</td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>
Validating the Novel Classification for the Assessment and Grading of Unexpected Events in Pediatric Surgery: The Clavien-Madadi Classification
ClinicalTrials.gov study NCT05778019. IPD Sharing: NO. Countries: 1. Publications: 5.
A Novel Classification for the Assessment and Grading of Unexpected Events in Pediatric Surgery: The Clavien-Madadi Classification
ClinicalTrials.gov study NCT05277441. IPD Sharing: NO. Countries: 1. Publications: 1.
The Implementation of the Classification of Intraoperative Adverse Events (ClassIntra) for Neurosurgical Procedures
ClinicalTrials.gov study NCT04956835. IPD Sharing: NO. Countries: 1. Publications: 2.
AVECL-UMONS DATABASE FOR AUDIO-VISUAL EVENT CLASSIFICATION AND LOCALIZATION
<p>AVECL-UMONS is a dataset for audio-visual event classification and localization in the context of office environments. The dataset is composed of 11 event classes recorded at several realistic positions in two different rooms. The dataset comprises two types of sequences according to the number of events in the sequence. 2662 unilabel sequences and 2724 multilabel sequences are recorded corresponding to a total of 5.24 hours.</p> <p>The dataset is complementary to the <em>SECL-UMons </em>dataset<em> </em>(also available on zenodo). The <em>SECL-UMons</em> dataset includes recordings made with a microphone array while <em>AVECL-UMONS</em> includes recordings made with 4 webcams.</p>
A Deep Learning-Based Approach for Efficient Detection and Classification of Local Ca²⁺ Release Events in Full-Frame Confocal Imaging
<p>U-Net implementation and project code: <a href="https://github.com/dottipr/sparks_project">https://github.com/dottipr/sparks_project</a></p> <p>GUI's github repository: <a href="https://github.com/r-janicek/xytCalciumSignalsDetection">https://github.com/r-janicek/xytCalciumSignalsDetection</a></p>
PYTHIA6 Dataset: Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics
<p>Dataset corresponding to: https://inspirehep.net/literature/2164495</p> <p>The data set contains PYTHIA6 jets in ep collisions, with separate files for LO DIS and photoproduction samples. For further details about the data set, see https://inspirehep.net/literature/2164495. Examples of how to analyze the data set can be found at: https://github.com/jdmulligan/ml-eic-flavor.</p> <p>Please contact james.mulligan@berkeley.edu with any questions.</p>
SECL-UMONS DATABASE FOR SOUND EVENT CLASSIFICATION AND LOCALIZATION
<p>SECL-UMons is a dataset for sound event classification and localization in the context of office environments. The multichannel dataset is composed of 11 event classes recorded at several realistic positions in two different rooms. The dataset comprises two types of sequences according to the number of events in the sequence. 2662 unilabel sequences and 2724 multilabel sequences are recorded corresponding to a total of 5.24 hours.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.