Musical Feature and Novelty Curve Characterizations as Predictors of Segmentation Accuracy

Novelty detection is a well-established method for analyzing the structure of music based on acoustic descriptors. Work on novelty-based segmentation prediction has mainly concentrated on enhancement of features and similarity matrices, novelty kernel computation and peak detection. Less attention, however, has been paid to characteristics of musical features and novelty curves, and their contribution to segmentation accuracy. This is particularly important as it can help unearth acoustic cues prompting perceptual segmentation and find new determinants of segmentation model performance. This study focused on spectral, rhythmic and harmonic prediction of perceptual segmentation density, whic…