Melodic Segmentation: Evaluating
the Performance of Algorithms and
Musical Experts
Belinda Thom, Christian Spevak, Karin Höthker
Institut für Logik, Komplexität und Deduktionssystene,
Universität Karlsruhe
Outline
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Segmentation Algorithms
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Segmentation Data
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Grouper
LBDM
Other Approaches
Essen Folk Song Collection
Musician Corpus
Experiments
Algorithm - Grouper
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Designed for monophonic music
Based on three rules:
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Gap Rule
Phrase Length Rule
Metrical Parallelism Rule
Calculates a gap score for pairs of notes
Assigns a penalty to boundaries different from an
ideal length
Combines local view with higher level metric
structure
Can handle MIDI data
Algorithm - LBDM
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Assigns boundary strength to pairs of notes
Quantifies how discontinuous each note pair is
Based on two rules:
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Change Rule
Proximity Rule
Boundary strength calculations provide insight
Can handle MIDI data
Algorithm – Other Approaches
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Memory-based approach
Consideration of harmonic structure
Rule-based system
Based on multi-layer neural network
Segmentation Data
Essen Folk Song Collection
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European Folk Songs
EsAC format – recording meter and key, pitches and
durations
Includes phrase boundaries
Musician Corpus
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10 musical excerpts chosen
19 musicians identified boundaries and phrase and
sub-phrase levels
Experiments
How Ambiguous is the Segmentation Task?
Level of ambiguity varies significantly based on music.
How Flexible is an Algorithm?
Grouper’s results agree more with the musicians
segmentations. Yet, LBDM performs better in certain
cases.
How Stable is an Algorithm?
Decrease in performance with unexpected changes,
especially in sub-phrases.
Grouper performs slightly better.