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- W4225856875 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." @default.
- W4225856875 created "2022-05-05" @default.
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- W4225856875 date "2021-11-04" @default.
- W4225856875 modified "2023-10-17" @default.
- W4225856875 title "Generating Diverse Realistic Laughter for Interactive Art" @default.
- W4225856875 doi "https://doi.org/10.48550/arxiv.2111.03146" @default.
- W4225856875 hasPublicationYear "2021" @default.
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