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.
5
datasets available to search
ShareScore release 0.9.0
Dataset results
5 results for “RAVDESS”
Test splits for CREMA-D, emoDB, IEMOCAP, MELD, RAVDESS
<p>Test splits for the categorical emotion datasets CREMA-D, emoDB, IEMOCAP, MELD, RAVDESS used inside audEERING. </p><p>For each dataset, a CSV file is provided listing the file names included in the test split.</p><p>The test splits were designed trying to balance gender and emotional categories as good as possible.</p>
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)
<p><strong>Description</strong></p> <p>The Ryerson Audio-Visual Database of Emotional Speech and Song (<a href="https://affectivedatascience.com/datasets.html#ravdess">RAVDESS</a>) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound). Note, there are no song files for Actor_18.</p> <p>The RAVDESS was developed by Dr <a href="https://affectivedatascience.com/people/livingstone_sr" target="_blank" rel="noopener">Steven R. Livingstone</a>, who now leads the <a href="https://affectivedatascience.com">Affective Data Science Lab</a>, and Dr <a href="https://www.torontomu.ca/psychology/about-us/our-people/faculty/frank-russo/" target="_blank" rel="noopener">Frank A. Russo</a> who leads the <a href="https://psychlabs.torontomu.ca/smartlab/">SMART Lab</a>.</p> <p><strong>Citing the RAVDESS</strong></p> <p>The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form. Published academic papers should use the academic paper citation for our PLoS1 paper. Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated.</p> <p><em>Academic paper citation</em></p> <blockquote> <p>Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> </blockquote> <p><em>Personal use citation</em></p> <blockquote> <p>Include a link to this Zenodo page - <a href="../record/1188976">https://zenodo.org/record/1188976</a></p> </blockquote> <p><strong>Commercial Licenses</strong></p> <p>Commercial licenses for the RAVDESS can be purchased. For more information, please visit our <a href="https://psychlabs.torontomu.ca/smartlab/ravdess-commercial-licensing/">license page of fees</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com</a>.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20feedback%20from%20Zenodo">ravdess@gmail.com</a>.</p> <p><strong>Example Videos</strong></p> <p>Watch a sample of the RAVDESS <a href="https://www.youtube.com/watch?v=Y7OQoNEu3dY">speech</a> and <a href="https://www.youtube.com/watch?v=XQkmH4oYZkg">song</a> videos.</p> <p><strong>Emotion Classification Users</strong></p> <p>If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>].</p> <p><strong>Construction and Validation</strong></p> <p>Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> <p>The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196391">PLoS ONE</a>.</p> <p><strong>Contents</strong></p> <p><em>Audio-only files</em></p> <p>Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):</p> <ul> <li>Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440. </li> <li>Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.</li> </ul> <p><em>Audio-Visual and Video-only files</em></p> <p>Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads:</p> <ul> <li>Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.</li> <li>Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.</li> </ul> <p><em>File Summary</em></p> <p>In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).</p> <p><strong>File naming convention</strong></p> <p>Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics: <br><br><em>Filename identifiers </em></p> <ul> <li>Modality (01 = full-AV, 02 = video-only, 03 = audio-only).</li> <li>Vocal channel (01 = speech, 02 = song).</li> <li>Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).</li> <li>Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.</li> <li>Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").</li> <li>Repetition (01 = 1st repetition, 02 = 2nd repetition).</li> <li>Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).</li> </ul> <p><br><em>Filename example: 02-01-06-01-02-01-12.mp4 </em></p> <ol> <li>Video-only (02)</li> <li>Speech (01)</li> <li>Fearful (06)</li> <li>Normal intensity (01)</li> <li>Statement "dogs" (02)</li> <li>1st Repetition (01)</li> <li>12th Actor (12)</li> <li>Female, as the actor ID number is even.</li> </ol> <p><strong>License information</strong></p> <p>The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> </p> <p>Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our <a href="https://smartlaboratory.org/ravdess/commercial-licensing/">license fee page</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com.</a></p> <p><strong>Related Data sets</strong></p> <ul> <li>RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>].</li> </ul>
RAVDESS Facial Landmark Tracking
<p><strong>Contact Information</strong></p> <p>If you would like further information about the RAVDESS Facial Landmark Tracking data set, or if you experience any issues downloading files, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20feedback%20from%20Zenodo">ravdess@gmail.com</a>. </p> <p><strong>Tracking Examples</strong></p> <p>Watch a sample of the <a href="https://youtu.be/cxMK2J0P7J0">facial tracking results</a>.</p> <p><strong>Description</strong></p> <p>The <a href="https://affectivedatascience.com/datasets.html#landmark-tracking">RAVDESS Facial Landmark Tracking dataset</a> set contains tracked facial landmark movements from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) [<a href="https://zenodo.org/record/1188976">RAVDESS Zenodo page</a>]. Motion tracking of actors' faces was produced by OpenFace 2.1.0 (Baltrusaitis, T., Zadeh, A., Lim, Y. C., & Morency, L. P., 2018). Tracked information includes: facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.</p> <p>The Facial Landmark Tracking dataset was created in the <a href="https://affectivedatascience.com">Affective Data Science Lab</a>.</p> <p>This data set contains tracking for all 2452 RAVDESS trials. All tracking movement data are contained in "FacialTracking_Actors_01-24.zip", which contains 2452 .CSV files. Each actor has 104 tracked trials (60 speech, 44 song). Note, there are no song files for Actor 18.</p> <p>Total Tracked Files = (24 Actors x 60 Speech trials) + (23 Actors x 44 Song trials) = 2452 files.</p> <p>Tracking results for each trial are provided as individual comma separated value files (CSV format). File naming convention of tracked files is identical to that of the RAVDESS. For example, tracked file "01-01-01-01-01-01-01.csv" corresponds to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4". For a complete description of the RAVDESS file naming convention and experimental manipulations, please see the <a href="https://zenodo.org/record/1188976">RAVDESS Zenodo page</a>. </p> <p>Tracking overlay videos for all trials are also provided (720p Xvid, .avi), one zip file per Actor. As the RAVDESS does not contain "ground truth" facial landmark locations, the overlay videos provide a visual 'sanity check' for researchers to confirm the general accuracy of the tracking results. The file naming convention of tracking overlay videos also matches that of the RAVDESS. For example, tracking video "01-01-01-01-01-01-01.avi" corresponds to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4".</p> <p><strong>Tracking File Output Format</strong></p> <p>This data set retained OpenFace's data output format, <a href="https://github.com/TadasBaltrusaitis/OpenFace/wiki/Output-Format">described here in detail</a>. The resolution of all input videos was 1280x720. When tracking output units are in pixels, their range of values is (0,0) (top left corner) to (1280,720) (bottom right corner). </p> <p><em>Columns 1-3 = Timing and Detection Confidence</em></p> <ul> <li>1. Frame - The number of the frame (source videos 30 fps), range = 1 to n</li> <li>2. Timestamp - Time of frame, range = 0 to m</li> <li>3. Confidence - Tracker confidence level in current landmark detection estimate, range = 0 to 1</li> </ul> <p><em>Columns 4-291 = Eye Gaze Detection</em></p> <ul> <li>4-6. gaze_0_x, gaze_0_y, gaze_0_z - Eye gaze direction vector in world coordinates for eye 0 (normalized), eye 0 is the leftmost eye in the image (think of it as a ray going from the left eye in the image in the direction of the eye gaze).</li> <li>7-9. gaze_1_x, gaze_1_y, gaze_1_z - Eye gaze direction vector in world coordinates for eye 1 (normalized), eye 1 is the rightmost eye in the image (think of it as a ray going from the right eye in the image in the direction of the eye gaze).</li> <li>10-11. gaze_angle_x, gaze_angle_y - Eye gaze direction in radians in world coordinates, averaged for both eyes. If a person is looking left-right this will results in the change of gaze_angle_x (from positive to negative) and, if a person is looking up-down this will result in change of gaze_angle_y (from negative to positive), if a person is looking straight ahead both of the angles will be close to 0 (within measurement error).</li> <li>12-123. eye_lmk_x_0, ..., eye_lmk_x55, eye_lmk_y_0,..., eye_lmk_y_55 - Location of 2D eye region landmarks in pixels. A figure describing the landmark index <a href="https://raw.githubusercontent.com/wiki/TadasBaltrusaitis/OpenFace/images/eye_lmk_markup.png">can be found here</a>.</li> <li>124-291. eye_lmk_X_0, ..., eye_lmk_X55, eye_lmk_Y_0,..., eye_lmk_Y_55,..., eye_lmk_Z_0,..., eye_lmk_Z_55 - Location of 3D eye region landmarks in millimeters. A figure describing the landmark index <a href="https://raw.githubusercontent.com/wiki/TadasBaltrusaitis/OpenFace/images/eye_lmk_markup.png">can be found here</a>.</li> </ul> <p><em>Columns 292-297 = Head pose</em></p> <ul> <li>292-294. pose_Tx, pose_Ty, pose_Tz - Location of the head with respect to camera in millimeters (positive Z is away from the camera).</li> <li>295-297. pose_Rx, pose_Ry, pose_Rz - Rotation of the head in radians around X,Y,Z axes with the convention R = Rx * Ry * Rz, left-handed positive sign. This can be seen as pitch (Rx), yaw (Ry), and roll (Rz). The rotation is in world coordinates with the camera being located at the origin.</li> </ul> <p><em>Columns 298-433 = Facial Landmarks locations in 2D</em></p> <ul> <li>298-433. x_0, ..., x_67, y_0,...y_67 - Location of 2D landmarks in pixels. A figure describing the landmark index <a href="https://raw.githubusercontent.com/TadasBaltrusaitis/OpenFace/master/imgs/landmark_scheme_68.png">can be found here</a>.</li> </ul> <p><em>Columns 434-637 = Facial Landmarks locations in 3D</em></p> <ul> <li>434-637. X_0, ..., X_67, Y_0,..., Y_67, Z_0,..., Z_67 - Location of 3D landmarks in millimetres. A figure describing the landmark index <a href="https://raw.githubusercontent.com/TadasBaltrusaitis/OpenFace/master/imgs/landmark_scheme_68.png">can be found here</a>. For these values to be accurate, OpenFace needs to have good estimates for fx,fy,cx,cy.</li> </ul> <p><em>Columns 638-677 = Rigid and non-rigid shape parameters</em></p> <p>Parameters of a point distribution model (PDM) that describe the rigid face shape (location, scale and rotation) and non-rigid face shape (deformation due to expression and identity). For more details, please refer to chapter 4.2 of my Tadas Baltrusaitis's PhD thesis [<a href="https://www.repository.cam.ac.uk/handle/1810/245253">download link</a>].</p> <ul> <li>638-643. p_scale, p_rx, p_ry, p_rz, p_tx, p_ty - Scale, rotation, and translation terms of the PDM.</li> <li>644-677. p_0, ..., p_33 - Non-rigid shape parameters.</li> </ul> <p><em>Columns 687-712 = Facial Action Units</em></p> <p>Facial Action Units (AUs) are a way to describe human facial movements (Ekman, Friesen, and Hager, 2002) [<a href="https://en.wikipedia.org/wiki/Facial_Action_Coding_System">wiki link</a>]. More information on OpenFace's implementation of AUs <a href="https://github.com/TadasBaltrusaitis/OpenFace/wiki/Action-Units">can be found here</a>.</p> <ul> <li>678-694. 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 - Intensity of AU movement, range from 0 (no muscle contraction) to 5 (maximal muscle contraction).</li> <li>695-712. 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 - Presence or absence of 18 AUs, range 0 (absent, not detected) to 1 (present, detected).</li> </ul> <p>Note, OpenFace's columns 2 and 5 (face_id and success, respectively) were not included in this data set. These values were redundant as a single face was detected in all frames, in all 2452 trials.</p> <p><strong>Tracking Overlay Videos</strong></p> <p>Tracking overlay videos visualize most aspects of the tracking output described above.</p> <ul> <li>Frame - Column 1, Top left corner of video</li> <li>Eye Gaze - Columns 4-11. Indicated by green ray emanating from left and right eyes.</li> <li>Eye region landmarks 2D - Columns 12-123. Red landmarks around left and right eyes, and black circles surrounding left and right irises.</li> <li>Head pose - Columns 292-297. Blue bounding box surrounding the actor's head.</li> <li>Facial landmarks 2D - Columns 298-433. Red landmarks on the participant's left and right eyebrows, nose, lips, and jaw.</li> <li>Facial Action Unit Intensity - Columns 687-694. All 17 AUs are listed on the left side of the video in black text. Intensity level (0-5) of each AU is indicated by the numeric value and blue bar.</li> <li>Facial Action Unit Presence - Columns 695-712. All 18 AUs are listed on the right side of the video in black & green text. Absence of an AU (0) is in black text with the numeric value 0.0. Presence of an AU (1) is in green text with the numeric value 1.0.</li> </ul> <p><strong>Camera Parameters and 3D Calibration Procedure</strong></p> <p>This data set contains accurate estimates of actors' 3D head poses. To produce these, camera parameters at the time of recording were required (distance from camera to actor, and camera field of view). These values were used with OpenCV's camera calibration procedure, <a href="https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_calib3d/py_calibration/py_calibration.html">described here</a>, to produce estimates of the camera's focal length and optical center at the time of actor recordings. The four values produced by the calibration procedure (fx,fy,cx,cy) were input to OpenFace as command line arguments during facial tracking, <a href="https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments">described here</a>, to produce accurate estimates of 3D head pose.</p> <p><em>Camera Parameters</em></p> <ul> <li>Distance between camera and actor = 1.4 meters</li> <li>Camera field of view = 0.5 meters</li> <li>Focal length in x (fx) = 6385.9</li> <li>Focal length in y (fy) = 6339.6</li> <li>Optical center in x (cx) = 824.241</li> <li>Optical center in y (cy) = 1033.6</li> </ul> <p>The use of OpenCV's calibration procedure was required as the video camera used in the RAVDESS recordings did not report focal length values. Unlike SLR cameras, most video cameras do not provide this information to the user due to their dynamic focus feature. For all RAVDESS recordings, camera distance, field of view, and focal point (manual fixed camera focus) were kept constant.</p> <p><strong>License information</strong></p> <p>The RAVDESS Facial Landmark Tracking data set is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NA-SC 4.0</a>.</p> <p><strong>How to cite the RAVDESS Facial Tracking data set</strong></p> <p><em>Academic citation </em><br>If you use the RAVDESS Facial Tracking data set in an academic publication, please cite both references: </p> <ol> <li>Swanson, R., Livingstone, SR., & Russo, FA. (2019). RAVDESS Facial Landmark Tracking (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3255102</li> <li>Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</li> </ol> <p><em>All other attributions </em><br>If you use the RAVDESS in a form other than an academic publication, such as in a blog post, school project, or non-commercial product, please use the following attribution: "<a href="https://zenodo.org/record/3255102">RAVDESS Facial Landmark Tracking</a>" by Swanson, Livingstone, & Russo is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NA-SC 4.0</a>.</p> <p><strong>Related Data sets</strong></p> <ul> <li>The Ryerson Audio-Visual Database of Emotional Speech and Song [<a href="https://zenodo.org/record/1188976">Zenodo project page</a>].</li> </ul>
Enhanced RAVDESS Speech Dataset
<p>This is a modified version of the speech audio contained within the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. The original dataset can be found <a href="https://zenodo.org/record/1188976#.YKvCMKhKhPY">here</a>. The unmodified version of just the speech audio used as source material for this dataset can be found <a href="https://www.kaggle.com/uwrfkaggler/ravdess-emotional-speech-audio">here</a>. This dataset performs speech enhancement and bandwidth extension on the original speech using HiFi-GAN. HiFi-GAN produces high-quality speech at 48 kHz that contains significantly less noise and reverb relative to the original recordings.</p> <p>If you use this work as part of an academic publication, please cite the papers corresponding to both the original dataset as well as HiFi-GAN:</p> <blockquote> <p>Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> <p>Su, Jiaqi, Zeyu Jin, and Adam Finkelstein. "HiFi-GAN: High-fidelity denoising and dereverberation based on speech deep features in adversarial networks." <em>Proc. Interspeech</em>. October 2020.</p> </blockquote> <p>Note that there are two recent papers with the name "HiFi-GAN". Please be sure to cite the correct paper as listed here.</p>
RAVDESS Speech 16K
<p>This is the 16kHz-version of RAVDESS Speech dataset. The original version is sampled at 48kHz. See link below for detail.</p> <p><a href="../records/1188976">https://zenodo.org/records/1188976 </a></p> <p>Why 16k version?</p> <ul> <li>Most deep learning model requires 16kHz of sampling rate.</li> <li>Lower sampling rate saves disk (70 MB vs 200 MB).</li> </ul>
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.