A brief presentation on techniques to compute the similarity between melodic pieces.

1. Symbolic Melodic Similarity
(through Shape Similarity)
Julián Urbano
Barcelona, November 12th 2013

2. Spam
• @julian_urbano
• PhD, Computer Science
– (Evaluation in) (Music) Information Retrieval
• Postdoctoral researcher
– Music Technology Group
– Universitat Pompeu Fabra
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3. Outline
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Symbolic Melodic Similarity
Representing Melodies
Comparing Melodies
Melodic Similarity through Shape Similarity
Results
Demo
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4. SYMBOLIC MELODIC SIMILARITY

5. Symbolic Melodic Similarity
• Symbolic
– No audio signals, just notes
• Melodic
– No polyphony
– Usually no harmony
– Usually a single voice
• Similarity
– Sound alike
– In terms of…good question!
– Very ill-defined: “whatever criteria you think makes two
melodies sound alike”
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6. Typical Use Case
• A user has a (large) collection of melodies and a
query melody
• An SMS system ranks all melodies in the
collection according to their estimated melodic
similarity to the query melody
– Implicitly, similarity is considered non-binary
• Two melodies are similar to each other to some degree
• Similarity is not a binary yes-no relation
– Usually just the top-k most similar
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7. Desired: Transposition Invariance
• Ignore “irrelevant” differences in pitch
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8. Desired: Transposition Invariance
• Ignore “irrelevant” differences in pitch
– Same note, different octave
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9. Desired: Transposition Invariance
• Ignore “irrelevant” differences in pitch
– Same note, different octave
– Same degree, different key
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10. Desired: Transposition Invariance
• Ignore “irrelevant” differences in pitch
– Same note, different octave
– Same degree, different key
– Same pitch, different key
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11. Desired: Time-scale Invariance
• Ignore “irrelevant” differences in rhythm
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12. Desired: Time-scale Invariance
• Ignore “irrelevant” differences in rhythm
– Same duration, different time signature
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13. Desired: Time-scale Invariance
• Ignore “irrelevant” differences in rhythm
– Same duration, different time signature
– Same duration, different tempo
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14. Desired: No Metadata
• Try not to rely on metadata, because it is not
always available
– Obvious: artist, genre, etc.
– Less-obvious: key, time signature, tempo, etc.
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15. REPRESENTING AND COMPARING MELODIES

16. (Bad) Representations of Melodies
• Transposition Invariance
– Pitches: from 0 to 127, in semitones
• C4 A4 A#5 = 60 69 82
– Directed interval with tonic, in semitones
• Tonic = C4: D4 A4 E3 = +2 +9 -8
– Ignore octaves
• C4 D4 = 2 , C4 E4 = 4 , C4 E3 = 4
– Parsons Code
• C4 D4 D4 E3 = U R D
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17. (Bad) Representations of Melodies
• Time-scale Invariance
– Actual duration, in milliseconds
• 250ms 187ms
– Metric duration
• Crotchet quaver = 1 1/2
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18. (Good) Representation of Melodies
• Represent relation between successive notes
– Directed pitch interval
• C4 D4 E4 E3 = +2 +2 -12
– Duration ratio
• 250ms 500ms 190ms 570ms = 2 0.37 3
• Crotchet quaver quaver whole = 0.5 1 4
• Allows us to detect and keep changes locally
+3 +2 +3 -3 -2 +2 +7 -2 -3 -4 +2 -5 +3 +2 +3 -3 -2
1 1 1 1 1 6 2/3 1 1 1 1 1/2 1 1 1 1 1
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19. Models of Similarity
• Given two melodies A and B…
– Represent them following these criteria
– But how do we compute their similarity?
• Several types of models
– Melody as text, classic retrieval
– Melody as text, editing distance
– Melody as graphs or trees, transition similarity
– Melody as n-grams, classic retrieval
– Melody as set of points, geometric similarities
– Melody as orthogonal chains, area between chains
– Melody as curves, alignment with geometric similarities
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20. Melody as (sequence of) Text
• Melodies are just a string of text, much like
sentences in a book
• Classic retrieval: compare frequencies of symbols
– C4 appears 5% of times, D6 does 0.38% of times, etc.
– Compare on a pitch-by-pitch basis
• Bag-of-words: loses sense of order in music
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21. Melody as (sequence of) Text
• Melodies are just a string of text, much like
sentences in a book
• Editing distance: how many transformations are
needed to go from melody A to melody B?
– C7 E7
A6 C7
– C7 A6 D7 A6 C7
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22. Melody as Graphs
• Melodies are a graph representing the
probability of transitions from pitch to pitch
– Compare on a transition-by-transition basis
D7
D7
E7
0.1
0.02
E7
0.1
0.1
0.1
0.08
G3
C7
0.15
E4
0.09
0.1
0.09
G3
C7
0.02
E4
0.09
0.01
0.01
• Sort-of sense of order
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23. Melody as (sequence of) n-grams
• Overlapping sequences of n successive notes
– C7 E7 A6 C7 D7
• n=2 : (C7 E7) (E7 A6) (A6 C7) (C7 D7)
• n=3 : (C7 E7 A6) (E7 A6 C7) (A6 C7 D7)
• n=4 : (C7 E7 A6 C7) (E7 A6 C7 D7)
• Classic retrieval: compare frequencies of n-grams
• Motifs, though n is fixed
• Sort-of sense of order
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24. Melody as (set of) Points
• Notes are points in the pitch-time plane, spaced
according to the pitch and time representation
– Compare disposition of points (eg. EMD)
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25. Melody as Orthogonal Chain
• Represent melodies in a stairway fashion
– Compare area between chains, looking for optimum
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26. Alignment of Symbols
• Independent of representation
• Alignment: similar to editing distance, but weight
differently insertions, deletions and mismatches
– C7
E7 A6 C7
– C7 A6 D7 A6 C7
(mismatch E7-D7 “costs” less than mismatch A6-E7)
• Problem: how to decide on weights
– Usually just compare numbers (pitch, octave, etc.)
– Some apply music theory
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27. MELODIC SIMILARITY THROUGH SHAPE SIMILARITY

28. Melody as a Curve
• Distribute notes as points in the pitch-time plane,
spaced according to their relative representation
• Calculate the curve interpolating those points
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29. Intuition Behind
• Two melodies are as similar as their curves are
• First derivative measures how much change
there is in pitch at any given point in time
– Nicely models the “change in music”
• Area between derivatives measures how much
the two melodies differ in their change of pitch
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30. Intuition Behind
• Naturally meets all transposition and time-scale
invariance requirements
• Naturally keeps sense of order in music
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31. Interpolation
• Many (boring) details regarding type of
interpolation
– Lagrange polynomials
– Splines
• Type
• Degree
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32. Interpolation
• We chose Uniform B-Splines of various degrees
– Changes are kept local
– Easy to compute
– Easy to differentiate (polynomials)
• Degrees 2 and 3 seemed to work better
– Computed in a span-by-span basis
• Sense of motifs, like n-grams
– Easily adaptable to differences in length
– Easily incorporate several dimensions of music beyond
pitch and time
– Easily compute similarity per dimension (partial derivative)
– Many other nice properties in terms of extreme behavior
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33. Implementation
• Represent melodies A and B with relative
distances between two successive notes
• Compute splines spans
– Same as compute n-grams and then smaller splines
• Align spline spans
– Weights computed from similarity in shape of spans
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34. Two Approaches
• Based on geometric properties and
characteristics of the collection
– Reward matches depending on average similarity
between spline spans in the collection
– Penalize insertions and deletions based on geometric
overall change with respect to x-axis
– Penalize mismatches based on geometric similarity of
spline spans
• Again, many (not so boring) details…
– Normalization of dimensions and weights
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35. Two Approaches
• How common spline spans are
– Reward matches and penalize insertions and removals
based on how common the spline spans are
• The more common, the smaller the penalization and the
higher the reward
– Penalize mismatches based on geometric similarity of
spline spans
• Roughly compare the direction of the curves
• Compute the area between derivatives
• Pitch and time together or separately
– Weight time differently, perceived as less important
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36. Alignment
• Local: don’t penalize changes at the beginning
and at the end of melodies
• Global: penalize changes everywhere
• Hybrid: penalize at the beginning, but don’t
penalize at the end
– Listeners pay attention to the beginning the most
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37. DOES ALL OF THIS EVEN WORK?
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38. Evaluation of Systems
• (Kind of) yearly at MIREX
– Collection with >2000 melodies
– Select query melodies
– Run systems
– Humans listen to the retrieved melodies and judge
how similar they are to the queries
– Systems are scored accordingly
• From 0 to 1 (perfect)
• And once again, many (not at all boring!) details…
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39. 2005
• Our algorithms didn’t participate, but anyway…
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40. 2005
• Our algorithms didn’t participate, but anyway…
best participant
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41. 2010
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42. 2011
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43. 2012
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44. 2013
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45. Briefly…
• Alignment works better
– Hybrid alignment works much better
• Geometric representations work better
• Incorporating rhythm in the comparison does not
improve much, and it’s computationally more
expensive
– Slightly improves ranking though
• Compare curves very roughly, without computing
areas between them
– Additionally, it is computationally more efficient
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46. HANDS-ON DEMO

47. MelodyShape
• A Java library and tool implementing all the
algorithms submitted to MIREX
https://github.com/julian-urbano/melodyshape
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48. MelodyShape
• A Java library and tool implementing all the
algorithms submitted to MIREX
https://github.com/julian-urbano/melodyshape
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49. MelodyShape
• A Java library and tool implementing all the
algorithms submitted to MIREX
https://github.com/julian-urbano/melodyshape
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50. Two Libraries Needed
here
...
or here
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51. To Run
• Just download everything to the same folder
– melodyshape-1.1.jar
– commons-math3-3.2.jar
– commons-cli-1.2.jar
• And double click
– melodyshape-1.1.jar
• Can also run from the
command line
• All your melodies
must be in MIDI format
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52. References
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• Bainbridge, D., Dewsnip, M., Witten, I.H.: Searching Digital Music Libraries. Information Processing and Management 41(1), 41–56 (2005)
• de Boor, C.: A Practical guide to Splines. Springer, Heidelberg (2001)
• Byrd, D., Crawford, T.: Problems of Music Information Retrieval in the Real World. Information Processing and Management 38(2), 249–272 (2002)
• Casey, M.A., Veltkamp, R.C., Goto, M., Leman, M., Rhodes, C., Slaney, M.: Content-Based Music Information Retrieval: Current Directions and Future Challenges. Proceedings of the IEEE 96(4),
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• Clifford, R., Christodoulakis, M., Crawford, T., Meredith, D., Wiggins, G.: A Fast, Randomised, Maximal Subset Matching Algorithm for Document-Level Music Retrieval.In: International
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• Doraisamy, S., Rüger, S.: Robust Polyphonic Music Retrieval with N-grams. Journal of Intelligent Systems 21(1), 53–70 (2003)
• Downie, J.S.: The Scientific Evaluation of Music Information Retrieval Systems: Foundations and Future. Computer Music Journal 28(2), 12–23 (2004)
• Downie, J. S., Ehmann, A. F., Bay, M., & Jones, M. C.: The Music Information Retrieval Evaluation eXchange: Some Observations and Insights. In W. R. Zbigniew & A. A. Wieczorkowska (Eds.),
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• Hanna, P., Ferraro, P., Robine, M.: On Optimizing the Editing Algorithms for Evaluating Similarity Between Monophonic Musical Sequences. Journal of New Music Research 36(4), 267–279 (2007)
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