Difference between revisions of "MIR workshop 2014"

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<b>Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval</b>
 
<b>Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval</b>
  
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=NEW PAGE FOR 2014=
 
  
 
== Logistics ==
 
== Logistics ==
 
Workshop Title: '''Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval'''
 
Workshop Title: '''Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval'''
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SEE 2015 WORKSHOP FOR THE LATEST INFORMATION: https://ccrma.stanford.edu/wiki/MIR_workshop_2014
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* Monday, June 23, through Friday, June 27, 2014. 9:30 AM to 5 PM every day.
 
* Monday, June 23, through Friday, June 27, 2014. 9:30 AM to 5 PM every day.
 
* Location: The Knoll, CCRMA, Stanford University. http://goo.gl/maps/nNKx
 
* Location: The Knoll, CCRMA, Stanford University. http://goo.gl/maps/nNKx
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'''Glossary of Terms to be used in this course <work in progress>'''
 
'''Glossary of Terms to be used in this course <work in progress>'''
  
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<br><u>Day 1: Part 1</u> [http://ccrma.stanford.edu/workshops/mir2013/CCRMA_MIR2013_Lecture1.pdf Lecture 1 Slides]
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<br><u>Day 1: Part 1</u> [http://ccrma.stanford.edu/workshops/mir2014/CCRMA_MIR2014_Lecture1.pdf Lecture 1 Slides]
  
 
* Introductions   
 
* Introductions   
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* CCRMA Introduction - (Carr/Sasha).   
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* CCRMA Introduction - (Nette, Carr, Fernando).   
 
* Introduction to MIR (What is MIR? Why are people interested? Commercial Applications of MIR)   
 
* Introduction to MIR (What is MIR? Why are people interested? Commercial Applications of MIR)   
 
* Overview of a basic MIR system architecture     
 
* Overview of a basic MIR system architecture     
 
* Timing and Segmentation: Frames, Onsets       
 
* Timing and Segmentation: Frames, Onsets       
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* Features: ZCR, Spectral moments; Scaling of feature data 
 
 
* Demo: Using simple heuristics and thresholds (i.e. "Why do we need machine learning?")
 
* Demo: Using simple heuristics and thresholds (i.e. "Why do we need machine learning?")
 
* Classification: Instance-based classifiers (k-NN)   
 
* Classification: Instance-based classifiers (k-NN)   
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** Classifier evaluation (Cross-validation, training and test sets)  
 
** Classifier evaluation (Cross-validation, training and test sets)  
  
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<br><u>Day 1: Part 2</u> [http://ccrma.stanford.edu/workshops/mir2013/CCRMA_MIR2013_Lecture2.pdf Lecture 2 Slides]
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<br><u>Day 1: Part 2</u> [http://ccrma.stanford.edu/workshops/mir2014/CCRMA_MIR2014_Lecture2.pdf Lecture 2 Slides]
  
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* Overview: Signal Analysis and Feature Extraction for MIR Applications (Historical: http://quod.lib.umich.edu/cgi/p/pod/dod-idx?c=icmc;idno=bbp2372.1999.