1. A Comparison of Manual and Automatic Melody Segmentation Massimo Melucci Nicola Orio

2. Introduction Content-based music retrieval. Content-based music retrieval. Random Segmentation, N-grams-based segmentation. Random Segmentation, N-grams-based segmentation. Detect boundaries to highlight musical phrases that describe music content. Detect boundaries to highlight musical phrases that describe music content.

3. Experiment Manual segmentation of a set of 20 scores by 17 expert musicians. 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. 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. Use statistical tools ( Cluster Analysis & Multidimensional Scaling) to measure degree of closeness between subjects.

4. Boundary Detection Random variable Y i = ( Y i,0,Y i,1, Y i,2 ) describes 2 3 outcomes of inserting markers around ‘i’. Random variable Y i = ( Y i,0,Y i,1, Y i,2 ) describes 2 3 outcomes of inserting markers around ‘i’. Marker around a note implies that a boundary exists around it. Marker around a note implies that a boundary exists around it. R i = 1 ( boundary at note i iff atleast 1 marker around ‘i’). R i = 1 ( boundary at note i iff atleast 1 marker around ‘i’). 0 ( no boundary iff no marker around ‘i’).

5. Boundary Detection (contd) Hypothesis that a boundary exists at note Hypothesis that a boundary exists at note ‘i’ is given by ‘i’ is given by Pr (R i =1 | X i ) > Pr (R i =0 | X i ) Pr (R i =1 | X i ) > Pr (R i =0 | X i ) Where X i =(X i 1,…..,X i Ns ) is the set of outcomes. Outcomes when R i =1 :- { (0,0,1), ( 0,1,0), ( 1,0,0), (0,1,1), ( 1,1,1)..} { (0,0,1), ( 0,1,0), ( 1,0,0), (0,1,1), ( 1,1,1)..}

6. Cluster Analysis and Multidimensional Scaling Figure 1 Figure 1

7. Performance of Automatic Segmenters Technique used for text segmentation in Topic Detection and Tracking (TDT). Technique used for text segmentation in Topic Detection and Tracking (TDT). P agree = sum [ D(i,j)*m S (i,j) *m A (i,j) ] P agree = sum [ D(i,j)*m S (i,j) *m A (i,j) ] AlgorithmP missP falseP disagree LBDM0.0540.3420.189 Random0.6530.3040.476 Fixed (N = 8 )0.4210.5580.485 Fixed (N =15)0.720.2860.517

8. Conclusion & Future Work Incorporation of melodic features in segmentation algorithm yields better results than those that do not. 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. Considering other features such as timbre, rhythm and harmony might be helpful. Effect of melodic features in query segmentation. Effect of melodic features in query segmentation.