ESLCE: A Dataset of Emotional Sounds from Large Crowd Events

• Published in: 2021 Joint 10th International Conference on Informatics, Electronics & Vision (ICIEV) and 2021 5th International Conference on Imaging, Vision & Pattern Recognition (icIVPR) pp. 1 - 7
• Main Authors: Atick Faisal, Md Ahasan, Ahmed, Mosabber Uddin, Rahman Ahad, Md Atiqur
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.08.2021
• Subjects: Crowd Emotion Recognition; Crowd Sound Dataset; Emotion recognition; Emotional Sound Dataset; Feature extraction; Machine learning; Organizations; Reliability; Speech processing; Speech recognition
• DOI: 10.1109/ICIEVicIVPR52578.2021.9564179

The Human-like ability to recognize emotion from speech has been an interesting field of research for quite a while now. In contrast, the emotion recognition from the sound of the crowd is a relatively new domain. Crowds express emotion as a collective group where the individual sounds combine together to make up emotions like cheering, booing, clapping, etc. As a result, recognizing emotion from crowd sound is very different from recognizing emotion from an individual's speech. Moreover, the lack of any large and diverse dataset makes it harder to perform machine learning analysis in this domain. In this paper, we present a relatively large and diverse dataset of the emotional sound of crowds collected from 70 different large crowd events. We collected data for 3 different types of emotion and organized the dataset into 5 different folds each containing a unique set of events. The diversity and organization will ensure the reliability of a machine learning model trained on this dataset. We also discuss the effectiveness of 34 different features and 2 analysis techniques on the proposed dataset. The dataset has been made publicly available for the community.