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Identifying Successful Melodic Similarity Algorithms for use in Music
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Identifying Successful Melodic Similarity Algorithms for use in Music



Margaret Cahill, Donncha Ó Maidín

Margaret Cahill, Donncha Ó Maidín
Centre for Computational Musicology and Computer Music, Department of Computer Science and Information Systems, University of Limerick, Ireland.



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Identifying Successful Melodic Similarity Algorithms for use in Music Identifying Successful Melodic Similarity Algorithms for use in Music Presentation Transcript

  • Identifying Successful Melodic Similarity Algorithms for use in Music Retrieval Margaret Cahill Donncha Ó Maidín University of Limerick, Ireland. [email_address]
  • Introduction
    • MIR - Music Information Retrieval
    • Field of research concerned with searching digitized collections of music
    • Various formats:
      • raw and compressed audio
      • scores
      • MIDI
  • Musical Scores
    • This research is concerned with music in score format
    • Collections of scores online e.g.
      • KernScores http://kern.humdrum.org/
      • MuseData http://www.musedata.org/
    • Music notation editors and scanning software allow saving/exporting in score format
  • Melodic Similarity
    • The ability to detect similar melodies allows:
      • searching for query melodies
      • analysis of compositional style
      • analysis of musical form/structure
      • identification of recurring themes and motifs
  • Current Approaches
    • Many current algorithms are based on string-matching techniques from the IR world
      • e.g. edit-distance algorithm
      • calculates the cost of turning one string into another using delete, replace, and insert actions
    • Operations suited to text are not always suited to melody
  • Features of a score
    • A wide range of features are available from a melodic score:
    • Which features
    • are important?
    • How should they
    • be combined?
    • Which algorithms
    • are suitable?
    Table 1: Some of the musical features available from a score implicit pattern of strong and weak stresses – metrical stress beaming ties tempo directions note duration phrasing accidentals ornamentation note pitch dynamics instrumentation articulation key signature rest time signature barlines clef
  • Our Approach
    • Use music perception research as a guide to choosing suitable features
    • Use a test-bed of melodies for which we already know the degree of similarity
    • Identify potential algorithms
    • Test use of features, method of combining features, values of internal weights and parameters
    • Tweak algorithms to improve performance
  • The Listening Experiment
    • Variations on Twinkle, Twinkle, Little Star for recorder by Duschenes.
    • Theme and Variations provides a natural range of similar melodies.
    • Provides a context for the listener – all melodies are related to the Theme.
    • The Theme is 8 bars long.
    • 9 variations.
    • Good examples of some musical issues melodic similarity algorithms face:
      • different time signatures
      • different keys
      • augmentation of theme (1 bar stretched to 2 bars)
      • triplets
      • single notes replaced by repeated rhythmic repetitions
      • ornate elaborations of the theme using both stepwise movements and arpeggic leaps
  • Theme Figure 1: The first 2 bars of the Theme and Variations
  • Listening Experiment
    • The Theme and each variation easily segments into 4-bar phrases.
    • Two 4-bar segments were extracted to form Part A and Part B of the experiment.
    • Pairs of melodies
    • played
    • Similarity was rated
    • on a 7-point scale.
    • Questionnaire and
    • comment sheet
    • Time = 24-30 minutes.
    • 34 subjects participated.
    Table 2: Structure of the listening experiment
  • Consistency of Results
    • Spearman’s correlation coefficient (correlation on the ranks) used to measure the consistency of subjects.
      • used the similarity rating given by subjects for the sequential and random (repeat) playing of melodies.
    • Only retained data from highly-correlated subjects:
      • >.789, significant at the .01 level
      • Part A: 26 subjects.
      • Part B: 16 subjects.
    • Ratings for random (repeat) playing discarded following the consistency check.
  • Consistency of Results
    • Inter-subject correlation used as
    • a rough measure of the overall
    • reliability of the data.
      • calculated from the correlation
      • matrices of each section
      • how each subject correlated
      • with every other subject
    • Median ratings used for
    • comparison with algorithmic
    • measures of similarity.
    Table 3: Inter-subject correlation Table 4: Results of listening experiment – median ratings.
  • The Algorithms where: k = the time windows of the score p 1 , p 2 = pitch values of the first and second melodies in a window w k = the weight associated with that time window totaldur = the duration processed Figure 2: Algorithm - Ó Maidín 1998
  • The Algorithms
    • processes the score in time windows
      • the duration of the shortest full note at that particular point in the score.
    • sum of the absolute pitch differences in each window multiplied by a corresponding weight for that window
    • weights can be duration, metrical accents,or combinations of these and other weighting factors
    • currently exploring variations of this algorithm using different combinations of musical features
    • transposition identified using the median value of the pitch differences with the associated weight value (similar to using frequency in statistics)
    • edit distance algorithm to be explored next
  • Comparing the Algorithmic and Human Measures of Similarity
    • Two main approaches taken
    • Correlation
      • gives an overall measure of performance across all variations rather than individual variations
    • Normalize data, then apply metric
      • z-scores or min-max normalization
      • can compare the algorithm performance for
      • individual variations
      • metric used to measure performance across
      • all variations
    Figure 3: Possible metrics for use with normalized data
  • Current Work
    • Have identified the use of duration and metrical stress weights for melodies by comparing algorithmic and human measure of similarity
    • Investigating the use of duration and metrical stress weights for melodies by comparing the algorithmic and human measures of similarity
    • Analyzing results through correlation and other metrics to determine which features are most successful for use in melodic similarity algorithms