Investigating the use of music features in audio-based emotion detection in laughter

• Main Authors: Canillas, Ramon Miguel F., Lachica, Joshua Daniel G., Sy, Paula Myles C.
• Format: text
• Language: English
• Published: Animo Repository 2013
• Subjects: Laughter; Philippine wit and humor; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/11108
• Institution: De La Salle University

Laughter is one of the pan-human expressive acts. It is a powerful affective and social signal since people very often express their emotion and regulate conversations by laughing. Current works on laughter focus on analyzing it through the use of spectral and prosodic features. The differentiating factor in this work is the use of music features. We investigate the usefulness of using music features for the analysis of laughter and discrimination of emotion. We perform different building, feature extraction and modeling and validation. All of this was geared towards determining the effectiveness of music features. Results indicate that while prosodic and spectral features are good in discriminating emotions, music features does an equal and/or even better job, showing its usefulness in this area of computing.