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5 results for “Video and audio dataset”
Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children (Openpose ADOS Dataset)
<p>Here, we share a de-identify subsample of the data used in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308388">research article</a>, that will allow interested scientists to test the <a href="https://github.com/AutismBrainBehavior/Video-Neural-Network-ASD-screening">shared code</a>, as well as, develop alternatives for achieving better prediction accuracy. We have prepared a subsample of pose estimation videos for the first 10 minutes of ADOS examination videos for each of the 160 children including in the current study (80 ASD and 80 TD, 80 Training set and 80 Testing set).</p> <p>With this subset of the full dataset, our trained model achieved an accuracy of 68.75% over 80 videos (40 ASD & 40 TD) by training the Visual Geometry Group 16 Long short term memory recurrent neural network (VGG16 LSTM RNN) over 80 training videos (40 ASD & 40 TD) at 64 batch size and 120 epochs.</p>
Accompanying dataset for "Nappe oscillations on free-overfall structures, data from laboratory experiments (audio and video)"
<p>This dataset accompanies the manuscript "Nappe Oscillations on Free-Overfall Structures: Data from Laboratory Experiments" submitted to Scientific Data.</p> <p>This dataset contains raw audio and video data, which complement the dataset uploaded at: <a href="https://zenodo.org/record/3381078#.XlUGVSFKiUk">https://zenodo.org/record/3381078#.XlUGVSFKiUk</a></p> <p>The names of the folders describe each one of the 52 experiments, with respect to the submitted paper in Scientific Data:</p> <p>- M1 and M2 denote Model 1 and Model 2, respectively.</p> <p>- C and UC denote confined and unconfined nappe, respectively.</p> <p>- QR, THR, HR, R, and RR denote the crest type of the weir as explained in the paper.</p> <p>- W is the width of the crest and L is the falling height.</p>
VidTIMIT Audio-Video Dataset
<p>See http://conradsanderson.id.au/vidtimit/ for details.<br> <br> Summary: Video and corresponding audio recordings of 43 people, reciting short sentences. Useful for research on topics such as automatic lip reading, multi-view face recognition, multi-modal speech recognition and person identification.</p> <p> </p>
WebRTC-QoE: A Dataset of Quality of Experience in Audio-Video Communications
<p>In the realm of real-time communications, WebRTC-based multimedia applications are increasingly prevalent as these can be smoothly integrated within Web browsing sessions. The browsing experience is then significantly improved concerning scenarios where browser add-ons and/or plug-ins are used; still, the end user's Quality of Experience (QoE) in WebRTC sessions may be affected by network impairments, such as delays and losses. Due to the variability in user perceptions under different communications scenarios, comprehending and enhancing the resulting service quality is a complex endeavour. To address this, we present a dataset that provides a comprehensive perspective on the conversational quality of a two-party WebRTC-based audiovisual telemeeting service. This dataset was gathered through subjective evaluations involving 20 subjects across 15 different test conditions (TCs). A specialized system was developed to induce controlled network disruptions such as delay, jitter, and packet loss rate, which adversely affected the communication between the parties. This methodology offered insight into user perceptions under various network impairments. The dataset encompasses a blend of objective and subjective data, including ACR (Absolute Category Rating) subjective scores, <em>webrtc-internals</em> parameters, facial expressions features, and speech features. Consequently, it serves as a substantial contribution to the improvement of WebRTC-based video call systems, offering practical and real-world data that can drive the development of more robust and efficient multimedia communication systems, thereby enhancing the user’s experience.</p> <div> <p> </p> <p> In the following, we discuss the details of the provided datasets.</p> <p><strong>Subjective_results_dataset.csv:</strong> This dataset encompasses subjective evaluation results from 20 subjects (users) who assessed the quality of WebRTC-based video calls under 15 distinct test conditions (TCs), which included combinations of 3 network impairments (delay, jitter, packet loss) to disturb the communication. The single discrete Absolute Category Rating (ACR) scale with five category labels (1-Bad, 2-Poor, 3-Fair, 4-Good, and 5-Excellent) was used by the users to rate the perceived QoE. A total of 300 ACR scores were obtained (20 participants x 15 TCs). The size of this dataset is 4.00 KB.</p> <p>The significance of each column is explained as follows:</p> <ul> <li>Test Condition (TC): It enumerates the TC numbers, which span from 1 to 15.</li> <li>Delay [ms]: It refers to the time it takes for a signal to travel from one point to another and is represented at three levels: 0 ms (no delay), 500 ms (moderate delay), and 1000 ms (significant delay).</li> <li>Jitter [ms]: It refers to the variability in delay and is represented at two levels: 0 ms (no jitter) and 500 ms (moderate jitter).</li> <li>Packet Loss Rate [%]: It refers to the loss of data packets during transmission and is represented at three levels: 0% (no packet loss), 15% (moderate packet loss), and 30% (significant packet loss).</li> <li>User: The users, identified from 1 to 20, participated in 15 video calls and evaluated the quality of these calls on a scale from 1 to 5.</li> </ul> <p><strong>Webrtc_internals_dataset.zip:</strong> This dataset contains text files collected using the webrtc-internals tool during the video calls. The zip is organized into 20 distinct folders, each labeled from ‘User1’ to ‘User20’. Each user folder contains 15 text files (.txt), each named following the pattern ‘webrtc_internals_dump-TCx_y-z-t.txt’. In this naming convention, ‘x’ denotes the TC number, ranging from 1 to 15. The ‘y-z-t’ segment varies with each TC, where ‘y’ signifies the delay value (0, 500, or 1000), ‘z’ indicates the jitter value (0 or 500), and ‘t’ represents the packet loss rate (0, 15, or 30). Each text file includes application-level data concerning WebRTC sessions' statistics in a JSON format. A total of 300 webrtc-internals dump text files were obtained (20 participants x 15 TCs). The size of this zip dataset is 28.7 MB (396 MB uncompressed). </p> <p><strong>Facial_expression_features_dataset.zip:</strong> This dataset contains facial expression features extracted from the recorded videos with face images using the OpenFace toolkit. The zip is organized into 20 distinct folders, each labelled from ‘User1’ to ‘User20’. Each user folder contains 15 .csv files, which are named following the pattern ‘TCx_y-z-t.csv’. In this naming convention, ‘x’ denotes the TC, ranging from 1 to 15. The ‘y-z-t’ segment varies with each TC, where ‘y’ signifies the delay value (0, 500, or 1000), ‘z’ indicates the jitter value (0 or 500), and ‘t’ represents the packet loss rate (0, 15, or 30). A total of 300 facial expression feature files in .csv format were obtained (20 participants x 15 TCs). For each face image of each TC, the OpenFace outputs 6 gaze direction features, 280 eye region landmarks and 35 Action Units (AUs). The size of this zip dataset is 547 MB (2.14 GB uncompressed). </p> <p>Each column carries a specific significance, which is elaborated as follows:</p> <ul> <li>frame: the frame number in the context of sequences.</li> <li>face_id: the identifier assigned to each face when multiple faces are present.</li> <li>timestamp: the elapsed time in seconds during the processing of a video sequence.</li> <li>confidence: the level of confidence the tracker has in the current landmark detection estimate.</li> <li>success: a face has been detected in the frame and it has been tracked accurately.</li> <li>gaze_0_x, gaze_0_y, gaze_0_z: the normalized eye gaze direction vector in world coordinates for eye 0, which is the eye on the left in the image.</li> <li>gaze_1_x, gaze_1_y, gaze_1_z: the normalized eye gaze direction vector in world coordinates for eye 1, which is the eye on the right in the image.</li> <li>gaze_angle_x, gaze_angle_y: the eye gaze direction, averaged for both eyes and expressed in world coordinates in radians, is converted into a format that is easier to use than gaze vectors.</li> <li>eye_lmk_x_0, eye_lmk_x_1, ..., eye_lmk_x55, eye_lmk_y_1, ... eye_lmk_y_55: the pixel coordinates of 2D landmarks in the eye region.</li> <li>eye_lmk_X_0, eye_lmk_X_1, ..., eye_lmk_X55, eye_lmk_Y_0, ..., eye_lmk_Z_55: the position of landmarks in the eye region in 3D space, measured in millimeters.</li> <li>17 AUr: detect the activation intensity (from 1 to 5) of a particular facial muscle. These are: AU01_r, AU02_r, AU04_r, AU05_r, AU06_r, AU07_r, AU09_r, AU10_r, AU12_r, AU14_r, AU15_r, AU17_r, AU20_r, AU23_r, AU25_r, AU26_r, AU45_r.</li> <li>18 AUc: Identify the activation of a particular muscle and note its presence (0 for absent, 1 for present). These are: AU01_c, AU02_c, AU04_c, AU05_c, AU06_c, AU07_c, AU09_c, AU10_c, AU12_c, AU14_c, AU15_c, AU17_c, AU20_c, AU23_c, AU25_c, AU26_c, AU28_c, AU45_c.</li> </ul> <p><strong>Speech_features_dataset.csv:</strong> This dataset is a robust assembly of speech features extracted from the recorded audio files using the OpenSMILE toolkit. The dataset is further enhanced with the integration of Absolute Category Rating (ACR) scores, which were assigned by each subject for every TC. These scores are embedded within the speech features of each subject. The dataset is exhaustive, encompassing a total of 1,911,900 speech features (calculated as 15 TCs x 20 subjects x 6373 speech features per subject). The total size of this dataset is 18.9 MB. </p> <p>Each column carries a specific significance, which is elaborated as follows:</p> <ul> <li>file: It presents the audio files from which the speech features were extracted.</li> <li>Users: The users, identified from 1 to 20, participated in 15 video calls and evaluated the quality of these calls on a scale from 1 to 5.</li> <li>Test Condition (TC): It enumerates the Test Condition numbers, which span from 1 to 15.</li> <li>Delay [ms]: It refers to the time it takes for a signal to travel from one point to another and is represented at three levels: 0 ms (no delay), 500 ms (moderate delay), and 1000 ms (significant delay).</li> <li>Jitter [ms]: It refers to the variability in delay and is represented at two levels: 0 ms (no jitter) and 500 ms (moderate jitter).</li> <li>Packet Loss Rate [%]: It refers to the loss of data packets during transmission and is represented at three levels: 0% (no packet loss), 15% (moderate packet loss), and 30% (significant packet loss).</li> <li>OpenSmile Speech Features Columns: This presents a detailed view of the speech features. Specifically, from column G to column IKI, it enumerates 6373 distinct speech features for each user under each TC of each processed audio file in .wav.</li> <li>ACR Score: The Absolute Category Rating (ACR) scale ranges from 1, representing the lowest quality, to 5, indicating the highest quality evaluated by the subjects.</li> </ul> <p> </p> <p><strong>If you make use of this dataset, please consider citing the following publication:</strong></p> <p>Bingol G., Porcu, S., Floris, A., & Atzori, L. (2024). WebRTC-QoE: A dataset of QoE assessment of subjective scores, network impairments, and facial & speech features. Computer Networks, 244, 110356, doi: 10.1016/j.comnet.2024.110356.</p> <p>BibTex format:</p> <p>@article{bingol2024datasetwebrtc, title={WebRTC-QoE: A dataset of QoE assessment of subjective scores, network impairments, and facial & speech features}, author={Bingol, Gulnaziye and Porcu, Simone and Floris, Alessandro and Atzori, Luigi}, journal={Computer Networks}, volume={244}, pages={110356}, year={2024}, publisher={Elsevier}, doi = {https://doi.org/10.1016/j.comnet.2024.110356} }</p> </div>
EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
<p>Understanding emotional states is pivotal for the development of next-generation human-machine interfaces. Human behaviors in social interactions have resulted in psycho-physiological processes influenced by perceptual inputs. Therefore, efforts to comprehend brain functions and human behavior could potentially catalyze the development of AI models with human-like attributes. In this study, we introduce a multimodal emotion dataset comprising data from 30-channel electroencephalography (EEG), audio, and video recordings from 42 participants. Each participant engaged in a cue-based conversation scenario, eliciting five distinct emotions: neutral, anger, happiness, sadness, and calmness. Throughout the experiment, each participant contributed 200 interactions, which encompassed both listening and speaking. This resulted in a cumulative total of 8,400 interactions across all participants. We evaluated the baseline performance of emotion recognition for each modality using established deep neural network (DNN) methods. The Emotion in EEG-Audio-Visual (EAV) dataset represents the first public dataset to incorporate three primary modalities for emotion recognition within a conversational context. We anticipate that this dataset will make significant contributions to the modeling of the human emotional process, encompassing both fundamental neuroscience and machine learning viewpoints.</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.