A comparison of manual and automatic melody segmentation
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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

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A comparison of manual and automatic melody segmentation

A Comparison of Manual and Automatic Melody Segmentation

Massimo Melucci

Nicola Orio


Introduction

Introduction

  • Content-based music retrieval.

  • Random Segmentation, N-grams-based segmentation.

  • Detect boundaries to highlight musical phrases that describe music content.


Experiment

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

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

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)..}


Cluster analysis and multidimensional scaling

Cluster Analysis and Multidimensional Scaling

  • Figure 1


Performance of automatic segmenters

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

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.


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