Abstract The 2020 COVID-19 pandemic and resulting national and international movement restrictions provide a unique opportunity to investigate the consequences of changing anthropogenic noise regimes on animal communities and soundscapes. Here I use this lockdown period as a natural experiment to investigate changes to soundscape intensity, structure, and dynamics during restricted human activity (lockdown) in suburban Nottingham, UK. Using 11 common acoustic indices, I tested for differences in the richness and evenness of the soundscape during COVID-19 lockdown, and I measured changes in soundscape dynamics by comparing the temporal variability of acoustic indices during versus after lockdown. Regardless of how the soundscape was summarised, there were significant differences in the intensity, evenness, and temporal variability of the soundscape during COVID-19 lockdown, principally driven by changes to anthropogenic noise. I recorded a shift away from a dominance of anthropophony towards more intense biological sounds during lockdown, and the lockdown soundscape was generally more even, particularly because of changes to the magnitude of the diurnal cycle. These preliminary results from a mass human confinement experiment provide an early glimpse into how suburban soundscapes are impacted by noise pollution. In time, globally distributed longer-term monitoring efforts will reveal the generality of these findings, facilitating a mechanistic understanding of the impacts of anthropogenic noise on the world’s natural and human-dominated soundscapes. Methods The dataset contains standardised acoustic index values for 11 commonly used acoustic indices, based on AudioMoth recordings taken during two periods around the COVID-19 lockdown (May 2020) and after restrictions had been lifted (Oct 2020) in suburban Nottingham, UK. I analysed the difference in acoustic index values during versus after the lockdown and compared their temporal variability using standardised effect sizes for the difference between these two time periods. I did this on the whole dataset and on several hourly subsets of the dataset (see the manuscript for further details). Usage notes See readme file for further details and main manuscript for descriptions of data.