Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)
The RAVDESS is a validated multimodal database of emotional speech and song. The set of 7356 files can be downloaded from the RAVDESS dataset. The set of recordings were evaluated by 319 individuals who were characteristic of untrained research participants from North America. High levels of emotional validity and intra-individual test-retest intrarater reliability were reported. All recordings are made freely available under a Creative Commons non-commercial license (CC BY-NA-SC 4.0).
PeakAffectDS
PeakAffectDS contains 663 files (total size: 1.84 GB), consisting of 612 physiology files, and 51 perceptual rating files. The dataset contains 51 untrained research participants (39 female, 12 male), who had their body physiology recorded while watching movie clips validated to induce strong emotional reactions. Emotional conditions included: calm, happy, sad, angry, fearful, and disgust; along with baseline a neutral condition. Four physiology channels were recorded with a Biopac MP36 system: two facial muscles with fEMG (zygomaticus major, corrugator supercilii) using Ag/AgCl electrodes, heart activity with ECG using a 1-Lead, Lead II configuration, and respiration with a wearable strain-gauge belt. While viewing movie clips, participants indicated in real-time when they experienced a "peak" emotional event, including: chills, tears, or the startle reflex. After each clip, participants further rated their felt emotional state using a forced-choice categorical response measure, along with their felt Arousal and Valence. All data are provided in plaintext (.csv) format.
Facial Expression and Landmark Tracking (FELT) dataset
The FELT 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.
RAVDESS Facial Landmark Tracking
This RAVDESS Facial Landmark Tracking dataset contains tracked facial landmark movements from the RAVDESS using OpenFace. Tracked information includes: facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.