Data cleansing and feature engineering on TAPLAB's tempo determination experiment. Implementing a sklearn pipeline upon model selection and hyper-parameter optimization to analyze significant musical characteristics within aggregated continuous tempo data. Explaining importance of contour change, stepsize, and leap on tempo with SHAP values and visualizations across 22 melodies and 5 starting tempos. Poster will be presented at the Society for Music Perception and Cognition Conference.
