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Melodic Segmentation Across Cultural Traditions M ARCELO E. R ODRGUEZ -L PEZ & A NJA V OLK Department of Information and Computing Sciences Utrecht University, Interaction Technology Group

  1. Melodic Segmentation Across Cultural Traditions M ARCELO E. R ODRÍGUEZ -L ÓPEZ & A NJA V OLK Department of Information and Computing Sciences Utrecht University, Interaction Technology Group ▼✳❊✳❘♦❞r✐❣✉❡③▲♦♣❡③❅✉✉✳♥❧ June 13, 2014

  2. What is Segmentation 2/38

  3. What is Segmentation Segmenting = Chunking = Grouping psychology : process of grouping individual units of information into larger units. computing : process of dividing data into smaller meaningful units. 3/38

  4. What is Segmentation Segmenting = Chunking = Grouping Psychology : process of grouping individual units of information into larger units. computing : process of dividing data into smaller meaningful units. 4/38

  5. What is Segmentation Segmenting = Chunking = Grouping Psychology : process of grouping individual units of information into larger units. Computing : process of dividing data into smaller meaningful units. 5/38

  6. Music Segmentation: DM Task Definition In Digital Musicology (DM) , segmentation is the task of dividing a musical piece/melody/section into ‘smaller’ structural units . 6/38

  7. Music Segmentation: Scope ◮ Type of Music and Segment Granularity - input : monophonic music (most generally melodies) - encoding : symbolic - music-theoretic segment parallel : phrases 7/38

  8. Melody Segmentation: an Example Aihu renmin zidibing & # 4 ## Q Q 2 Q EQ E Q H Q q . q q q q q q q q q q q q e Q q q 8/38

  9. Melody Segmentation: Task ◮ Task - identify segment boundary locations - pair boundaries (begin, end) - label boundaries 9/38

  10. Melody Segmentation: Task & # 4 ## Q Q 2 Q EQ E Q H Q q . q q q q q q q q q q q q e Q q q phrase boundary 10/38

  11. Models of Melody Segmentation - development : +30 years - # of models : ≈ 27 - comparative studies : 4, 3-6 models evaluated - most successful : Gestalt-based 11/38

  12. Gestalt Models: Basic Overview - attempt to quantify (visual) Gestalt principles - use system of preference rules - Gestalt proximity is modeled as ‘discontinuity’ detection (breaks in the melodic flow) 12/38

  13. Local Discontinuities Detection: an Example 13/38

  14. Local Discontinuities Detection: an Example Q Q Q EQ E Q H Q q . q q q q q q q q q q q q e Q q q 14/38

  15. Local Discontinuities Detection: an Example Q Q Q EQ E Q H Q q . q q q q q q q q q q q q e Q q q 15/38

  16. Local Discontinuities Detection: Input Q Q Q EQ E Q H Q q . q q q q q q q q q q q q e Q q q . . . . . . . . . . . . e 1 e i e N . . . . . . onset: 24 48 72 84 96 120 2322 pitch: 69 69 68 66 66 69 64 16/38

  17. Local Discontinuities Detection: Input Q Q Q EQ E Q H Q q . q q q q q q q q q q q q e Q q q ioi : .5 .5 .5 .25 .25 .5 .5 1 .5 .5 .5 .5 2 .75 .25 .5 .5 .5 1 .5 .5 .5 .5 .5 .5 p-inv : 0 1 2 0 3 5 0 5 1 2 3 2 5 2 2 3 3 5 2 3 7 1 2 3 5 17/38

  18. Local Discontinuity Detection: Profiles Q Q Q EQ E Q H Q q . q q q q q q q q q q q q e Q q q ioi : p-inv : 18/38

  19. Local Discontinuity Detection: Profiles Q Q Q EQ E Q H Q q . q q q q q q q q q q q q e Q q q ioi : p-inv : 19/38

  20. Local Discontinuity Detection: Output Q Q Q EQ E Q H Q q . q q q q q q q q q q q q e Q q q Output : 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 20/38

  21. Gestalt Models: Assumptions - discontinuity as a distance in some perceptual space - discontinuity can be treated as a local phenomenon - discontinuity is universal/idiom-independent 21/38

  22. Statistical Study: Research Questions - relevance of discontinuity as a boundary cue? - relevance of discontinuity parametric representation? - appropriateness of locality for modeling? - appropriateness of universality assumption? 22/38

  23. Statistical Study: Data E SSEN F OLK S ONG C OLLECTION (EFSC) - 20,000 annotated vocal folk melodies - Representative subsets of German and Chinese songs E XPERIMENTAL S UBSETS - ≈ 5 k German songs, ≈ 30 k phrases, 1 . . . 29 notes - ≈ 2 k Chinese songs, ≈ 12 k phrases, 1 . . . 169 notes F ILTERED - ≈ 1 . 2 k German songs, ≈ 5 . 5 k phr-pairs, 13 . . . 21 notes - ≈ 1 . 4 k Chinese songs, ≈ 4 . 6 k phr-pairs, 2 . . . 28 notes 23/38

  24. Statistical Study: Processing ph a ph b ## Q Q 2 Q EQ E Q & # H Q q . 4 q q q q q q q q q q q q e Q q q c(ph a) j(ph ab) c(ph b) Nioi : .35 .5 .15 p-inv : 2 5 2 24/38

  25. Results 1: pitch duration Germany China d - 32.41 % 43.73 % - d 49.57 % 72.56 % d - 04.51 % 22.53 % - d 24.13 % 57.70 % nd - 31.49 % 28.53 % - nd 23.26 % 09.04 % 25/38

  26. Results 1: pitch duration Germany China d - 32.41 % 43.73 % - d 49.57 % 72.56 % d - 04.51 % 22.53 % - d 24.13 % 57.70 % nd - 31.49 % 28.53 % - nd 23.26 % 09.04 % 26/38

  27. Results 1: pitch duration Germany China d - 32.41 % 43.73 % - d 49.57 % 72.56 % d - 04.51 % 22.53 % - d 24.13 % 57.70 % nd - 31.49 % 28.53 % - nd 23.26 % 09.04 % 27/38

  28. Results 1: pitch duration Germany China d - 32.41 % 43.73 % - d 49.57 % 72.56 % d - 04.51 % 22.53 % - d 24.13 % 57.70 % nd - 31.49 % 28.53 % - nd 23.26 % 09.04 % 28/38

  29. Results 2: Corpus: Germ any Corpus: China | case: All ph | case: All ph ab | Pitch Analysis ab | Pitch Analysis 20 20 PI (Semitones) 15 PI (Semitones) 15 10 10 5 5 0 0 J(pab) C(pa) C(pb) J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals 29/38

  30. Results 2: Corpus: Germ any Corpus: China | case: All ph | case: All ph ab | Pitch Analysis ab | Pitch Analysis 20 20 PI (Semitones) 15 PI (Semitones) 15 10 10 5 5 0 0 J(pab) C(pa) C(pb) J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals Corpus: Germ any Corpus: China | case: All ph ab | Duration Analysis | case: All ph ab | Duration Analysis 0.7 0.8 0.6 0.6 0.5 N IOI N IOI 0.4 0.4 0.3 0.2 0.2 0.1 J(pab) C(pa) C(pb) J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals 30/38

  31. Results 2: Corpus: Germ any Corpus: China | case: All ph | case: All ph ab | Pitch Analysis ab | Pitch Analysis 20 20 PI (Semitones) 15 PI (Semitones) 15 10 10 5 5 0 0 J(pab) C(pa) C(pb) J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals Corpus: Germ any Corpus: China | case: All ph ab | Duration Analysis | case: All ph ab | Duration Analysis 0.7 0.8 0.6 0.6 0.5 N IOI N IOI 0.4 0.4 0.3 0.2 0.2 0.1 J(pab) C(pa) C(pb) J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals 31/38

  32. Results 2: Corpus: Germ any Corpus: China | case: All ph | case: All ph ab | Pitch Analysis ab | Pitch Analysis 20 20 PI (Semitones) 15 PI (Semitones) 15 10 10 5 5 0 0 J(pab) C(pa) C(pb) J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals Corpus: Germ any Corpus: China | case: All ph ab | Duration Analysis | case: All ph ab | Duration Analysis 0.7 0.8 0.6 0.6 0.5 N IOI N IOI 0.4 0.4 0.3 0.2 0.2 0.1 J(pab) C(pa) C(pb) J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals 32/38

  33. Results 3: ◮ group phrase pairs in respect to size( ph a ) ◮ pairwise t-test between m- j ( ph a , b ) and m- c ( ph a , b ) ◮ China PI: 19/26, NIOI: 26/26 ◮ Germany PI: 6/19, NIOI: 15/19 33/38

  34. Results 3: Germanic Chinese 5 5 5 5 4.5 4.5 4.5 4.5 4 4 4 4 3.5 3.5 3.5 3.5 PI PI 3 3 3 3 2.5 2.5 2.5 2.5 2 2 2 2 1.5 1.5 1.5 1.5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 23 24 25 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 23 24 25 3 6 7 9 10 12 3 6 7 9 10 12 0.65 0.65 0.65 0.65 0.55 0.55 0.55 0.55 0.45 0.45 NIOI 0.45 NIOI 0.45 0.35 0.35 0.35 0.35 0.25 0.25 0.25 0.25 0.15 0.15 0.15 0.15 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 28 29 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 28 29 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 34/38 Length of ph a Length of ph a

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