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Identifying Successful Melodic Similarity Algorithms for use in Music Retrieval   Margaret Cahill Donncha Ó Maidín   University of Limerick, Ireland.  [email_address]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Musical Scores  ,[object Object],[object Object],[object Object],[object Object],[object Object]
Melodic Similarity ,[object Object],[object Object],[object Object],[object Object],[object Object]
Current Approaches ,[object Object],[object Object],[object Object],[object Object]
Features of a score ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],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 ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Listening Experiment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Theme Figure 1: The first 2 bars of the Theme and Variations
Listening Experiment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Table 2: Structure of the listening experiment
Consistency of Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Consistency of Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],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 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Comparing the Algorithmic and Human Measures of Similarity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Figure 3: Possible metrics for use with normalized data
Current Work ,[object Object],[object Object],[object Object]

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Identifying Successful Melodic Similarity Algorithms for use in Music

  • 1. Identifying Successful Melodic Similarity Algorithms for use in Music Retrieval Margaret Cahill Donncha Ó Maidín University of Limerick, Ireland. [email_address]
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  • 9. Theme Figure 1: The first 2 bars of the Theme and Variations
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  • 13. 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
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