Generating Diverse Realistic Laughter for Interactive Art

Autor:	Afsar, M. Mehdi, Park, Eric, Paquette, Étienne, Gidel, Gauthier, Mathewson, Kory W., Muller, Eilif
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	We propose an interactive art project to make those rendered invisible by the COVID-19 crisis and its concomitant solitude reappear through the welcome melody of laughter, and connections created and explored through advanced laughter synthesis approaches. However, the unconditional generation of the diversity of human emotional responses in high-quality auditory synthesis remains an open problem, with important implications for the application of these approaches in artistic settings. We developed LaughGANter, an approach to reproduce the diversity of human laughter using generative adversarial networks (GANs). When trained on a dataset of diverse laughter samples, LaughGANter generates diverse, high quality laughter samples, and learns a latent space suitable for emotional analysis and novel artistic applications such as latent mixing/interpolation and emotional transfer. Comment: Presented at Machine Learning for Creativity and Design workshop at NeurIPS 2021, 6 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2111.03146 Zobrazit plný text záznamu View this record from Arxiv