ESLCE: A Dataset of Emotional Sounds from Large Crowd Events

Abstract

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.