This study proposes a novel application of change-point techniques to the question of how dynamic markings in a score correspond to performed loudness. We apply and compare two change-point algorithms–Killick, Fearnhead, and Eckley's Pruned Exact Linear Time (PELT) method, and Scott and Knott's Binary Segmentation (BS) approach(-)to detecting changes in dynamics in recorded performances of Chopin's Mazurkas. Dynamic markings in the score, assumed to correspond to change points, serve as ground truth. The PELT algorithm has a higher average best F-measure (15.78 % for 0 tolerance threshold; 29 % for one-beat tolerance threshold) compared to the BS algorithm (10.94 % and 19.74 %, respectively), it also results in a smaller average Hausdorff distance–32.8 vs. 77 score beats for 0 tolerance and 32 vs. 52.2 score beats for one-beat tolerance. Applications of loudness change-point detection include audio-to-score transcription.