Generating Diverse Realistic Laughter for Interactive Art
This work addresses the challenge of synthesizing diverse human emotional responses in audio for artistic settings, though it appears incremental as it applies existing GAN methods to a specific domain.
The paper tackled the problem of generating diverse, high-quality laughter sounds for interactive art, using a GAN-based method called LaughGANter that produces realistic laughter samples and learns a latent space for emotional analysis and artistic applications.
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.