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)

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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