Exploring Laughter Sound Visualizations for Self Reflection

Yangyang Yang and Kimiko Ryokai. 2022. Exploring Laughter Sound Visualizations for Self Reflection. In Designing Interactive Systems Conference (DIS '22). Association for Computing Machinery, New York, NY, USA, 1472–1485. https://doi.org/10.1145/3532106.3533546


We present an exploratory design study on visualizing laughter sounds for personal reflection. We experimented with a variety of graphic design elements to visualize temporal, spatial, and social aspects of laughter sounds. In order to experience their own laughter being visualized, our participants collected audio recordings of everyday conversations with their loved ones. We extracted laughter from the participants’ audio files using a machine learning algorithm, then visualized selected laughter in five different types of visual representations, and shared the result with each participant. Through the journey of collecting, seeing, listening to, and interacting with their personal laughter visualizations, participants explored what laughter means for them in different contexts. The study reveals that interactive laughter visualizations have the potential to evoke memories, support emotional expressions, and promote relationships.

Last updated:

February 8, 2023