Fig. 4
From: Detecting fingering of overblown flute sound using sparse feature learning
MFCCs and sparse features with and without max-pooling projected onto a factorial map. The factorial map is built from the first and second highest eigenvalues of PCA and t-SNE (u 1, u 2) for visualization purposes. a PCA factorial map of five fingerings (C4, C5, E♭4, G4, G6) producing a G6 tone. b t-SNE factorial map of the same tones. It is possible to observe that sparse features are “cleaner” features for flute fingerings than the MFCCs. In addition, max-pooling removes a considerable amount of noise from the feature