In this paper, we advance an enhanced method for computing Harte et al.’s (2006) Harmonic Change Detection Function (HCDF), which aims to detect harmonic transitions in musical audio signals. Each of the HCDF component blocks is revisited in light of recent advances in harmonic description and transformation. To evaluate our proposal, we compute an exhaustive grid search to compare the multiple proposed algorithms and a large set of parameterizations across four large style-specific musical datasets. Our results show that the newly proposed methods and parameter optimization improve the detection of harmonic changes, by 5.57% (f-score) with respect to previous methods. Furthermore, while guaranteeing recall values at >99%, our other method improves precision by 6.28%.
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