gerhard widmer
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Using AI and Machine Learning to study Expressive Music Performance: Project Survey and First Report. Gerhard Widmer. Keywords:. Machine Learning Data Mining Expressive Music Performance. Outline. 1. Introduction 2. Expressive Music Performance 3. The Base Research Framework - PowerPoint PPT PresentationTRANSCRIPT
Using AI and Machine Learning to study Expressive Music Performance: Project Survey and First Report
Gerhard WidmerUsing AI and Machine Learning to study Expressive Music Performance:Project Survey and First Report
1Keywords:Machine Learning
Data Mining
Expressive Music Performance2Outline1. Introduction
2. Expressive Music Performance
3. The Base Research Framework
4. A Brief Activity and Status Report
5. A New Data Mining Approach
6. Conclusion1. Introduction4Apply AI methods
Developing computational methods
Use machine learning and data mining
Build formal models
ai,Machine learning and data mining.,,model52. Expressive Music Performance
6Make music moving
Detect patter and regularities
Performing artist
Large Collections of performances
,,,,,,,,,,,,,machine learning and data mining
73. Inducting Performance Models from Real performances : The Base Research Framework,model,:81. 2.
3. 4.(,)
5. 6.
7.9
104. A Brief Activity and Status Report114.1 Real-world Performance Data
It is impossible to extract precise performance information.
The main source of performance data are special pianos. ( Bosendorfer SE290)
,(,)cd,(),,
124.2 Score and Expression Extraction
Beat induction
Quantization
Inferring the correct or intended enharmonic spelling of notes.(eg : G# VS. Ab),,,(),(),,,,, ,,,; ,,,ga,,134.3 Musical Structure Analysis
Segmentation
Categorization and motiuic analysis
Implication Realization Model,,Segmentation model,model,,Categorization ,,(/),,Implication() ...,,
144.4 Mode Building via Inductive Learning : Initial Investigations
Settings of the rule parameters better then baseline.
Model:,,,(:)
155. Learning Partial Characterizing Models : A New Data Mining Approach165.1 The Goal : Learning Partial ModelsPartial Characterizing Models.,,175.2 Data and Target ConceptsIn the timing dimension
In dynamic
In articulation,13,;,(:,,)(:,),:Timing dimensionn,n,n,...Dynamics n ,..Articulation ,,0.8,,0.81,1185.3 The PLCG Rule Discovery Alogrithm
plogP,l,c,gDcLCdnd,L class c model R RR,r,ci,i=1...k ,c,ci:ri....t, ri t,ri ( c )195.4 Some Simple Principles Discovered
plcg,..1894,14.2%588,2.86%29641/5(22.11%)..205.5 Quantitative Evaluation
1data..2data21
322
1...1.,1..!23Conclusion
24machine learning & data mining
25The End26