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A Comparison of Manual and Automatic Melody Segmentation. Massimo Melucci Nicola Orio. Introduction. Content-based music retrieval. Random Segmentation, N-grams-based segmentation. Detect boundaries to highlight musical phrases that describe music content. Experiment.
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A Comparison of Manual and Automatic Melody Segmentation Massimo Melucci Nicola Orio
Introduction • Content-based music retrieval. • Random Segmentation, N-grams-based segmentation. • Detect boundaries to highlight musical phrases that describe music content.
Experiment • Manual segmentation of a set of 20 scores by 17 expert musicians. • Compare results with Automatic segmentation using probability of miss and probability of false alarm. • Use statistical tools ( Cluster Analysis & Multidimensional Scaling) to measure degree of closeness between subjects.
Boundary Detection • Random variable Yi= ( Yi,0 ,Yi,1, Yi,2) describes 23outcomes of inserting markers around ‘i’. • Marker around a note implies that a boundary exists around it. • Ri= 1 ( boundary at note i iff atleast 1 marker around ‘i’). 0 ( no boundary iff no marker around ‘i’).
Boundary Detection (contd) • Hypothesis that a boundary exists at note ‘i’ is given by Pr (Ri=1 | Xi) > Pr (Ri=0 | Xi) Where Xi=(Xi 1,…..,Xi Ns) is the set of outcomes. Outcomes when Ri=1 :- { (0,0,1) , ( 0,1,0), ( 1,0,0), (0,1,1), ( 1,1,1)..}
Performance of Automatic Segmenters • Technique used for text segmentation in Topic Detection and Tracking (TDT) . • Pagree= sum [ D(i,j)*mS(i,j) *mA(i,j) ]
Conclusion & Future Work • Incorporation of melodic features in segmentation algorithm yields better results than those that do not. • Considering other features such as timbre, rhythm and harmony might be helpful. • Effect of melodic features in query segmentation.
