Generating Diverse Realistic Laughter for Interactive Art

TL;DR

This paper introduces LaughGANter, a GAN-based method for generating diverse, high-quality human laughter to enhance interactive art and emotional expression, addressing the challenge of realistic auditory laughter synthesis.

Contribution

The paper presents LaughGANter, a novel GAN approach that produces diverse, realistic laughter samples and enables emotional analysis and artistic manipulation.

Findings

Generates diverse, high-quality laughter samples

Learns a latent space for emotional analysis

Enables artistic applications like interpolation and emotional transfer

Abstract

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.