Cities have changed profoundly as a result of the COVID-19 pandemic. Stay-at-home (SAH) policies have suddenly shut down schools, work, and leisure spaces, drastically reduced activities in supermarkets, bars, and restaurants. With most people at home, pedestrian movement has reduced to unprecedented lows in cities across the world.
In Sonic Cities, we reflect on the dynamic soundscapes of human activity and the natural environment in urban parks during the pandemic. Using machine learning techniques, we analyze the audio from walks taken in key parks around the world to recognize changes in sounds like human voices, emergency sirens, street music, sounds of nature (i.e., bird song, insects), dogs barking, and ambient city noise. We extracted audio files from YouTube videos of park walks from previous years, and compared them with walks recorded by volunteers along the same path during the COVID-19 pandemic. The analysis suggests an overall increase in birdsong and a decrease in city sounds, such as cars driving by, or construction work. The interactive visualization proposed in Sonic Cities allows users to explore and experience the changing soundscapes of urban parks.