Soundtrap usage during COVID-19: A machine-learning approach to assess the effects of the pandemic on online music learning

Abstract

The COVID-19 pandemic prompted a sudden rethinking of how music was taught and learned. Prior to the pandemic, the web-based digital audio workstation Soundtrap emerged as a leading platform for creating music online. The present study examined the growth of Soundtrap’s usage during the COVID-19 pandemic. Using machine-learning methods, we analyzed anonymized user data from Soundtrap’s 1.6 million educational users in the United States to see if the pandemic affected Soundtrap’s education user base and, if so, to what extent. An exploratory data analysis demonstrated a large increase in Soundtrap’s user base beyond five standard deviations beginning in March 2020. A subsequent changepoint analysis identified March 17, 2020, as the day this shift occurred. Finally, we created a SARIMAX model using data prior to March 17 to forecast expected growth. This model was unable to account for user growth after March 17, showing highly anomalous growth rates outside of the model’s confidence interval. We discuss how this shift affects music education practices and what it portends for our field. In addition, we explore the role of machine learning and artificial intelligence as a method for research in the music education field.