Harmonic Change Detection for musical chords segmentation

Abstract

In this paper, different strategies for the calculation of the Harte's Harmonic Change Detection Function (HCDF) are discussed. HCDFs can be used for detecting chord boundaries for Automatic Chord Estimation (ACE) tasks, where the chord transitions are identified as peaks in the HCDF. We show that different audio features and different novelty metric have significant impact on the overall accuracy results of a chord segmentation algorithm. Furthermore, we show that certain combination of audio features and novelty measures provide a significant improvement with respect to the current chord segmentation algorithms.