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Conditional Restricted Boltzmann Machinesfor Mono/Polyphonic Composer Identification
BNAIC 2015, November 6th 2015Johan Loeckx, Joeri Bultheel
Artificial Intelligence LabVrije Universiteit BrusselBelgium
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Ultimate goal
Given a symbolic representation of a composer's piece
Identify the composer
Based on a set of previous compositions
More specifically... Mozart vs. Haydn string quartets (contemporary composers with a similar style)
3/19
Outline
I. Computational representation of music➔ Existing representations➔ Subtleties in music
II. Methodology➔ Conditional RBMs➔ Topology➔ Dataset
III. Results & discussion➔ Accuracy➔ Training/test set division
IV. Conclusions
4/19
– I –Computational representations of music
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Computational representations
A lot of computational symbolic representations exist
Model type➔ Markov models➔ (Context-free) Grammars➔ Finite State Machines➔ Feature models➔ N-grams➔ ...
Issues ➔ Musicality / interpretation ➔ Cultural aspect vs. Cognitive ➔ Semantics vs. syntax➔ Discourse level
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Musical subtleties
Motion: different resolutions: hierarchy of ascending - descending
Seconds in high & low register
Pedal tones
In which key is this piece?
Long term “dependencies” (often with local corrections / interactions)
What is not there, matters
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Composer identification:State-of-the-art
Hand-crafted feature models (polyphonic) [1]➔ 5-fold cross-validation● 80% accuracy
N-gram models (monophonic) [2]➔ Leave-one-out ➔ 75.4% accuracy
When discriminating between Mozart & Haydn's string quartets...
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– II –Methodology
Do Conditional RBMs make good unsupervised feature extractors for composer identification?
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Conditional Restricted Boltzmann Machine
In our case a 10s “memory”, composed of 80 125ms quantized sections
of 16th notes
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Topology
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Dataset
Complete set of Haydn's & Mozart string quartets➔ Very similar musical style (classical)➔ Each string quartet consists of 3 movements➔ The data set was ordered by time of writing
Three scenario's were considered➔Scenario 0: all movements were shuffled➔Scenario I: test & training movement never belong to same quartet ➔Scenario II: all training data was “written” before test data
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– III –Results & Discussion
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Scenario 0
Monophonic binary prediction accuracy
Polyphonic binary prediction accuracy (voting)A voting scheme based upon the 4 monophonic predictions
above, yielded a 96% accuracy
5-fold cross-validation on movement-level
≈ baseline / similar for both composers (bass line most determined by style and not by the composer?)
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Scenario 1 (training & test movements never belong to same quartet)
(not all data used as a consequence)
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Scenario 2 (training movements written before the test movements)
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– IV –Conclusions
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Conclusions
Conditional RBMs useful as feature extractors➔Considerable improvement over the state-of-the-art
Style evolves within a composer's work➔ If the model is not trained on contemporary pieces (compared to the test set)
➔ If the model is trained on “older” pieces only
Future work➔ What does it teach us (insight?)
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References
[1] William Herlands, Ricky Der, Yoel Greenberg, and Simon Levin. A machine learning approach to musically meaningful homogeneous style classification. In Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.
[2] Ruben Hillewaere, Bernard Manderick, and Darrell Conklin. String quartet classification with monophonic models. In ISMIR, pages 537–542, 2010.