To obtain the complete dataset, please download the ZIP file.This dataset is divided into two part:Normal_Insulators contains the normal insulators capture by UAVs. The number of the normal insulator images is 600.Defective_Insulators contains the insulators with defect. The number of the defective insulator images is 248. Since we don't have too much defective insulators, the data augmentation method is applied. These images are synthesized by following process:Use the algorithm in TVSeg to segment the defective insulator from a small part original images, the segment results are the mask images;Use affine transform to augment the original images and their mask, the augmentation results is a lot of original-mask image pairs;Use these image pairs to train the U-Net;Use the trained U-Net to segment the rest part of images;Attach the insulators in different backgrounds.Both these two directories contain two subdirectories, one called images contains the image files, the other called labels contains the VOC2007 format annotations.The labels of Normal_Insulators contains only the annotations of insulators;The labels of Defective_Insulators contains not only the annotations of insulators but also the annotations of defects which on the insulators.The images is provided by the State Grid Corporation of China, and the dataset is made by WANG Zi-Hao. If you have any question about this dataset, feel free to contact zhwang0721@gmail.com.

Description

The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) 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.

The RAVDESS was developed by Dr Steven R. Livingstone, who now leads the Affective Data Science Lab, and Dr Frank A. Russo who leads the SMART Lab.

Citing the RAVDESS

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.

Academic paper citation

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. https://doi.org/10.1371/journal.pone.0196391.

Personal use citation

Include a link to this Zenodo page - https://zenodo.org/record/1188976

Commercial Licenses

Commercial licenses for the RAVDESS can be purchased. For more information, please visit our license page of fees, or contact us at ravdess@gmail.com.

Contact Information

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 ravdess@gmail.com.

Example Videos

Watch a sample of the RAVDESS speech and song videos.

Emotion Classification Users

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 [Zenodo project page].

Construction and Validation

Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - https://doi.org/10.1371/journal.pone.0196391.

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 PLoS ONE.

Contents

Audio-only files

Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):

• Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440.
• Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.

Audio-Visual and Video-only files

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:

• 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.
• 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.

File Summary

In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).

File naming convention

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:

Filename identifiers

• Modality (01 = full-AV, 02 = video-only, 03 = audio-only).
• Vocal channel (01 = speech, 02 = song).
• Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).
• Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.
• Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").
• Repetition (01 = 1st repetition, 02 = 2nd repetition).
• Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).

Filename example: 02-01-06-01-02-01-12.mp4

1. Video-only (02)
2. Speech (01)
3. Fearful (06)
4. Normal intensity (01)
5. Statement "dogs" (02)
6. 1st Repetition (01)
7. 12th Actor (12)
8. Female, as the actor ID number is even.

License information

The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0

Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our license fee page, or contact us at ravdess@gmail.com.

Related Data sets

• RAVDESS Facial Landmark Tracking data set [Zenodo project page].

Contact Information

If you would like further information about the Facial expression and landmark tracking data set, or if you experience any issues downloading files, please contact us at ravdess@gmail.com.

Facial Expression examples

Watch a sample of the facial expression tracking results.

Commercial Licenses

Commercial licenses for this dataset can be purchased. For more information, please contact us at ravdess@gmail.com.

Description

The Facial Expression and Landmark Tracking (FELT) dataset dataset contains tracked facial expression movements and animated videos from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) [RAVDESS Zenodo page]. Tracking data and videos were produced by Py-Feat 0.6.2 (2024-03-29 release) (Cheong, J.H., Jolly, E., Xie, T. et al. Py-Feat: Python Facial Expression Analysis Toolbox. Affec Sci 4, 781–796 (2023). https://doi.org/10.1007/s42761-023-00191-4) and custom code (github repo). Tracked information includes: facial emotion classification estimates, facial landmark detection (68 points), head pose estimation (yaw, pitch, roll, x, y), and facial Action Unit (AU) recognition. Videos include: landmark overlay videos, AU activation animations, and landmark plot animations.

The FELT dataset was created at the Affective Data Science Lab.

This dataset contains tracking data and videos for all 2452 RAVDESS trials. Raw and smoothed tracking data are provided. All tracking movement data are contained in the following archives: raw_motion_speech.zip, smoothed_motion_speech.zip, raw_motion_song.zip, and smoothed_motion_song.zip. Each actor has 104 tracked trials (60 speech, 44 song). Note, there are no song files for Actor 18.

Total Tracked Files = (24 Actors x 60 Speech trials) + (23 Actors x 44 Song trials) = 2452 CSV files.

Tracking results for each trial are provided as individual comma separated value files (CSV format). File naming convention of raw and smoothed tracked files is identical to that of the RAVDESS. For example, smoothed 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 RAVDESS Zenodo page.

Landmark overlays, AU activation, and landmark plot videos for all trials are also provided (720p h264, .mp4). Landmark overlays present tracked landmarks and head pose overlaid on the original RAVDESS actor video. 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. Landmark plot animations present landmarks only, anchored to the top left corner of the head bounding box with translational head motion removed. AU activation animations visualize intensity of AU activations (0-1 normalized) as a heatmap over time. The file naming convention of all videos also matches that of the RAVDESS. For example, "Landmark_Overlay/01-01-01-01-01-01-01.mp4", "Landmark_Plot/01-01-01-01-01-01-01.mp4", "ActionUnit_Animation/01-01-01-01-01-01-01.mp4", all correspond to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4".

Smoothing procedure

Raw tracking data were first low-pass filtered with a 5th order butterworth filter (cutoff_freq = 6, sampling_freq = 29.97, order = 5) to remove high-frequency noise. Data were then smoothed with a Savitzky-Golay filter (window_length = 11, poly_order = 5). Scipy.signal (v 1.13.1) was used for both procedures.

Landmark Tracking models

Six separate machine learning models were used by Py-Feat to perform various aspects of tracking and classification. Video outputs generated by different combinations of ML models were visually compared, with final model choice determined by voting of first and second authors. Models were specified in the call to Detector class (described here). Exact function call as follows:

Detector(face_model='img2pose',
landmark_model='mobilenet',
au_model='xgb',
emotion_model='resmasknet',
facepose_model='img2pose-c',
identity_model='facenet',
device='cuda',
n_jobs=1,
verbose=False,
)

Default Py_feat parameters to each model were used in most cases. Non-defaults were specified in the call to detect_video function (described here). Exact function call as follows:

(video_path,
skip_frames=None,
output_size=(720, 1280),
batch_size=5,
num_workers=0,
pin_memory=False,
face_detection_threshold=0.83,
face_identity_threshold=0.8
)

Tracking File Output Format

This data set retained Py-Feat's data output format. The resolution of all input videos was 1280x720. Tracking output units are in pixels, their range of values is (0,0) (top left corner) to (1280,720) (bottom right corner).

Column 1 = Timing information

• 1. frame - The number of the frame (source videos 29.97 fps), range = 1 to n

Columns 2-5 = Head bounding box

• 2-3. FaceRectX, FaceRectY - X and Y coordinates of top-left corner of head bounding box (pixels)
• 4-5. FaceRectWidth, FaceRectHeightF - Width and Height of head bounding box (pixels)

Column 6 = Face detection confidence

• FaceScore - Confidence level that a human face was deteceted, range = 0 to 1

Columns 7-142 = Facial landmark locations in 2D

• 7-142. x_0, ..., x_67, y_0,...y_67 - Location of 2D landmarks in pixels. A figure describing the landmark index can be found here.

Columns 143-145 = Head pose

• 143-145. Pitch, Roll, Yaw - Rotation of the head in degrees (described here). The rotation is in world coordinates with the camera being located at the origin.

Columns 146-165 = Facial Action Units

Facial Action Units (AUs) are a way to describe human facial movements (Ekman, Friesen, and Hager, 2002) [wiki link]. More information on Py-Feat's implementation of AUs can be found here.

• 145-150, 152-153, 155-158, 160-165. AU01, AU02, AU04, AU05, AU06, AU09, AU10, AU12, AU14, AU15, AU17, AU23, AU24, AU25, AU26, AU28, AU43 - Intensity of AU movement, range from 0 (no muscle contraction) to 1 (maximal muscle contraction).
• 151, 154, 159. AU07, AU11, AU20 - Presence or absence of AUs, range 0 (absent, not detected) to 1 (present, detected).

Columns 166-172 = Emotion classification confidence

• 162-172. anger, disgust, fear, happiness, sadness, surprise, neutral - Confidence of classified emotion category, range 0 (0%) to 1 (100%) confidence.

Columns 173-685 = Face identity score

Identity of faces contained in the video were classified using the FaceNet model (described here). This procedure generates at 512 dimension Euclidean embedding space.

• 173. Identity - Predicated individual identifyed in the RAVDESS video. Note, value is always Person_0, as each video only contains a single actor at all times (categorical).
• 174-685. Identity_1, ..., Identity_512 - Face embedding vector used by FaceNet to perform facial identity matching.

Column 686 = Input video

• 686. frame - The number of the frame (source videos 29.97 fps), range = 1 to n

Columns 687-688 = Timing information

• 687. frame.1 - The number of the frame (source videos 29.97 fps), duplicated column, range = 1 to n
• 688. approx_time - Approximate time of current frame (0.0 to x.x, in seconds)

Tracking videos

Landmark Overlay and Landmark Plot videos were produced with plot_detections function call (described here). This function generated invidual images for each frame, which were then compiled into a video using the imageio library (described here).

AU Activation videos were produced with plot_face function call (<a

The included tests were performed at McMaster University in Hamilton, Ontario, Canada by Dr. Phillip Kollmeyer (phillip.kollmeyer@gmail.com). If this data is utilized for any purpose, it should be appropriately referenced. -A brand new 3Ah LG HG2 cell was tested in an 8 cu.ft. thermal chamber with a 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale. these data are used in the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The data also includes a description of data acquisition, data preparation, development of an FNN example script.

-Instructions for Downloading and Running the Script: 1-Select download all files from the Mendeley Data page (https://data.mendeley.com/datasets/cp3473x7xv/2).
2-The files will be downloaded as a zip file. Unzip the file to a folder, do not modify the folder structure.
3-Navigate to the folder with "FNN_xEV_Li_ion_SOC_EstimatorScript_March_2020.mlx" 4-Open and run "FNN_xEV_Li_ion_SOC_EstimatorScript_March_2020.mlx" 5-The matlab script should run without any modification, if there is an issue it's likely due to the testing and training data not being in the expected place. 6-The script is set by default to train for 50 epochs and to repeat the training 3 times. This should take 5-10 minutes to execute. 7-To recreate the results in the paper, set number of epochs to 5500 and number of repetitions to 10.

-The test data, or similar data, has been used for some publications, including: [1] C. Vidal, P. Kollmeyer, M. Naguib, P. Malysz, O. Gross, and A. Emadi, “Robust xEV Battery State-of-Charge Estimator Design using Deep Neural Networks,” in Proc WCX SAE World Congress Experience, Detroit, MI, Apr 2020 [2] C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, "Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning," 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 2019, pp. 1-6.

Data Access: The data in the research collection provided may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use it only for research purposes. Due to these restrictions, the collection is not open data. Please download the Agreement at Data Sharing Agreement and send the signed form to fakenewstask@gmail.com .

Citation

Please cite our work as

@article{shahi2021overview,
 title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection},
 author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas},
 journal={Working Notes of CLEF},
 year={2021}
}

Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English.

Subtask 3A: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. The training data will be released in batches and roughly about 900 articles with the respective label. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. Our definitions for the categories are as follows:

• False - The main claim made in an article is untrue.

• Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.

• True - This rating indicates that the primary elements of the main claim are demonstrably true.

• Other- An article that cannot be categorised as true, false, or partially false due to lack of evidence about its claims. This category includes articles in dispute and unproven articles.

Subtask 3B: Topical Domain Classification of News Articles (English) Fact-checkers require background expertise to identify the truthfulness of an article. The categorisation will help to automate the sampling process from a stream of data. Given the text of a news article, determine the topical domain of the article (English). This is a classification problem. The task is to categorise fake news articles into six topical categories like health, election, crime, climate, election, education. This task will be offered for a subset of the data of Subtask 3A.

Input Data

The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:

Task 3a

• ID- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• our rating - class of the news article as false, partially false, true, other

Task 3b

• public_id- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• domain - domain of the given news article(applicable only for task B)

Output data format

Task 3a

• public_id- Unique identifier of the news article
• predicted_rating- predicted class

Sample File

public_id, predicted_rating
1, false
2, true

Task 3b

• public_id- Unique identifier of the news article
• predicted_domain- predicted domain

Sample file

public_id, predicted_domain
1, health
2, crime

Additional data for Training

To train your model, the participant can use additional data with a similar format; some datasets are available over the web. We don't provide the background truth for those datasets. For testing, we will not use any articles from other datasets. Some of the possible source:

• Fakenews Classification Datasets
• Fake News Detection Challenge KDD 2020
• FakeNewsNet

IMPORTANT!

1. Fake news article used for task 3b is a subset of task 3a.
2. We have used the data from 2010 to 2021, and the content of fake news is mixed up with several topics like election, COVID-19 etc.

Evaluation Metrics

This task is evaluated as a classification task. We will use the F1-macro measure for the ranking of teams. There is a limit of 5 runs (total and not per day), and only one person from a team is allowed to submit runs.

Submission Link: https://competitions.codalab.org/competitions/31238

Related Work

• Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf
• G. K. Shahi and D. Nandini, “FakeCovid – a multilingualcross-domain fact check news dataset for covid-19,” inWorkshop Proceedings of the 14th International AAAIConference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14
• Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104