Melodic Segmentation Across Cultural Traditions
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1 Melodic Segmentation Across Cultural Traditions MARCELO E. RODRÍGUEZ-LÓPEZ & ANJA VOLK Department of Information and Computing Sciences Utrecht University, Interaction Technology Group 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 ## & # 2 4 q q q q q q q q Aihu renmin zidibing q q q q H QQQ Q E Q E Q Qq q e q. 8/38
9 Melody Segmentation: Task Task - identify segment boundary locations - pair boundaries (begin, end) - label boundaries 9/38
10 Melody Segmentation: Task ## & # 2 4 q q q q q q q q q q q q H QQQ Q E Q E Q Qq q e 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 q q q q q q q q q H QQQ Q E Q E Q Qq q e q. 14/38
15 Local Discontinuities Detection: an Example q q q q q q q q q q q q H QQQ Q E Q E Q Qq q e q. 15/38
16 Local Discontinuities Detection: Input onset: pitch: q q q q q q H q q q q q q QQQ Q E Q E Q Qq q e q. e 1... e i e N /38
17 Local Discontinuities Detection: Input q q q q q q q q q q q q H QQQ Q E Q E Q Qq q e q. ioi : p-inv : /38
18 Local Discontinuity Detection: Profiles q q q q q q q q q q q q H QQQ Q E Q E Q Qq q e q. ioi : p-inv : 18/38
19 Local Discontinuity Detection: Profiles q q q q q q q q q q q q H QQQ Q E Q E Q Qq q e q. ioi : p-inv : 19/38
20 Local Discontinuity Detection: Output q q q q q q q q q q q q H QQQ Q E Q E Q Qq q e q. Output : /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 ESSEN FOLK SONG COLLECTION (EFSC) - 20,000 annotated vocal folk melodies - Representative subsets of German and Chinese songs EXPERIMENTAL SUBSETS - 5k German songs, 30k phrases, notes - 2k Chinese songs, 12k phrases, notes FILTERED - 1.2k German songs, 5.5k phr-pairs, notes - 1.4k Chinese songs, 4.6k phr-pairs, notes 23/38
24 Statistical Study: Processing ph a ## & # 2 4 q q q q q q q q ph b q q q q H QQQ Q E Q E Q Qq q e q. c(ph a) j(ph ab) c(ph b) Nioi : p-inv : /38
25 Results 1: pitch duration Germany China d % % - d % % d % % - d % % nd % % - nd % % 25/38
26 Results 1: pitch duration Germany China d % % - d % % d % % - d % % nd % % - nd % % 26/38
27 Results 1: pitch duration Germany China d % % - d % % d % % - d % % nd % % - nd % % 27/38
28 Results 1: pitch duration Germany China d % % - d % % d % % - d % % nd % % - nd % % 28/38
29 Results 2: Corpus: Germany case: All ph ab Pitch Analysis Corpus: China case: All ph ab Pitch Analysis PI (Semitones) PI (Semitones) J(pab) C(pa) C(pb) 0 J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals 29/38
30 Results 2: Corpus: Germany case: All ph ab Pitch Analysis Corpus: China case: All ph ab Pitch Analysis PI (Semitones) PI (Semitones) J(pab) C(pa) C(pb) 0 J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals Corpus: Germany case: All ph ab Duration Analysis Corpus: China case: All ph ab Duration Analysis N IOI N IOI 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: Germany case: All ph ab Pitch Analysis Corpus: China case: All ph ab Pitch Analysis PI (Semitones) PI (Semitones) J(pab) C(pa) C(pb) 0 J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals Corpus: Germany case: All ph ab Duration Analysis Corpus: China case: All ph ab Duration Analysis N IOI N IOI 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: Germany case: All ph ab Pitch Analysis Corpus: China case: All ph ab Pitch Analysis PI (Semitones) PI (Semitones) J(pab) C(pa) C(pb) 0 J(pab) C(pa) C(pb) Join Interval and Context Intervals Join Interval and Context Intervals Corpus: Germany case: All ph ab Duration Analysis Corpus: China case: All ph ab Duration Analysis N IOI N IOI 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: 5 5 Germanic 5 Chinese PI PI NIOI NIOI Length of ph a Length of ph a 34/38
35 Results 3: 5 5 Germanic 5 Chinese PI PI NIOI NIOI Length of ph a Length of ph a 35/38
36 Results 3: 5 5 Germanic 5 Chinese PI PI NIOI NIOI Length of ph a Length of ph a 36/38
37 Conclusions Under the scope of our study: discontinuities in pitch - are weak predictors of melodic phrase boundaries - can not be considered universal discontinuities in duration - are strong predictors of phrase boundaries - remain stable independent of cultural origin and size 37/38
38 THANK YOU! 38/38
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