Difference between revisions of "MIR workshop 2008"

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(potential workshop titles)
(workshop outline)
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Rhythm Analysis  
 
Rhythm Analysis  
** Onset Detection
+
* Onset Detection
** Beat Detection
+
* Beat Detection
** Meter detection
+
* Meter detection
  
 
Data reduction techniques
 
Data reduction techniques
** Linear regression  
+
* Linear regression  
** Threshold, Adaptive Threshold
+
* Threshold, Adaptive Threshold
** Peak Picking  
+
* Peak Picking  
** PCA / LDA
+
* PCA / LDA
** Feature Selection
+
* Feature Selection
  
 
Structure and Segmentation
 
Structure and Segmentation
** ASA 101
+
* ASA 101
  
 
Classification
 
Classification
** k-NN
+
* k-NN
** SVM
+
* SVM
** HMM
+
* HMM
** Neural Nets
+
* Neural Nets
  
 
Evaluation Methodology  
 
Evaluation Methodology  
** Data set construction
+
* Data set construction
** Feature selection
+
* Feature selection
** Cross Validation
+
* Cross Validation
** Information Retrieval metrics (precision, recall, F-Measure)  
+
* Information Retrieval metrics (precision, recall, F-Measure)  
  
 
Lab Exercises
 
Lab Exercises
** Feature extraction from audio
+
* Feature extraction from audio
** Instrument Identification
+
* Instrument Identification
** Real-time MIR (vowel v consonant?) with ChucK  
+
* Real-time MIR (vowel v consonant?) with ChucK  
 
 
 
Guest lecturer from a local music information retrieval startup?
 
Guest lecturer from a local music information retrieval startup?

Revision as of 19:25, 4 March 2008

CCRMA Workshop: Music Information Retrieval

This is Jay and Ge's brainstorming page for this summer's MIR workshop.

logistics

  • Summer 2008
  • Instructors: Jay LeBoeuf and Ge Wang


potential workshop titles

  • Music Information Retrieval
  • Information Retrieval in the Service of Music
  • Music Information Retrieval and Applications for Computer Audio
  • Intelligent Audio Systems : A review of the foundations and applications of Semantic Audio Analysis and Music Information Retrieval

== workshop outline == Introduction to Capabilities of MIR

Survey of the field, Real-world applications, and challenges

  • Current commercial applications
    • Music Recommender Systems
    • Playlisting systems
    • DJ systems
    • Music Transcription
    • DAW technologies
    • Band in a box
  • Large University projects

Signal Processing Basics (if necessary)

Feature Extraction

  • Low Level Features
    • ZCR
    • Spectral Moments (Centroid, Flux, Flatness, Kurtosis)
    • Spectral Bands / Filters
    • MFCC (Source-filter modeling)
    • MPEG-7
  • Higher-level features
    • Chroma features
    • Key estimation
    • Chords
    • Pitch Estimation
    • Genre (genre, artist ID, similarity)
    • "Fingerprints"

Rhythm Analysis

  • Onset Detection
  • Beat Detection
  • Meter detection

Data reduction techniques

  • Linear regression
  • Threshold, Adaptive Threshold
  • Peak Picking
  • PCA / LDA
  • Feature Selection

Structure and Segmentation

  • ASA 101

Classification

  • k-NN
  • SVM
  • HMM
  • Neural Nets

Evaluation Methodology

  • Data set construction
  • Feature selection
  • Cross Validation
  • Information Retrieval metrics (precision, recall, F-Measure)

Lab Exercises

  • Feature extraction from audio
  • Instrument Identification
  • Real-time MIR (vowel v consonant?) with ChucK

Guest lecturer from a local music information retrieval startup?

potential software, libraries, examples

  • MATLAB
  • ChucK / UAna
  • Marsyas
  • CLAM
  • Machine Learning Libraries
  • Weka Machine Learning and Data Mining Toolbox (Standalone app / Java)
  • Netlab Pattern Recognition and Clustering Toolbox (Matlab)
  • libsvm SVM toolbox (Matlab)

Abstract

Music Information Retrieval (MIR) is a highly-interdisciplinary field bridging the domains of digital audio signal processing and machine learning. Simply put, MIR algorithms allow a computer to “listen” and “understand” audio, such as MP3s in a personal music collection, live streaming audio, or gigabytes of sound effects. In the same way that listeners can recognize a song’s characteristics – tempo, key, chord progressions, genre, or song structure – MIR algorithms are capable of recognizing and extracting this information. By understanding the characteristics of an audio selection, we can perform extensive sorting, searching, music recommendation, metadata generation, and transcription of that audio.

This workshop will target students, researchers, and industry audio engineers who are unfamiliar with the field of Music Information Retrieval (MIR). We will demonstrate the myriad of exciting technologies enabled by the fusion of basic signal processing techniques with machine learning and pattern recognition. The presentations will be applied, multimedia-rich, overview of the building blocks of modern MIR systems. Our goal is to make highly-interdisciplinary technologies and dauntingly-complex algorithms approachable.

The workshop will consist of half-day lectures, half-day supervised lab sessions, and classroom exercises and discussions.

Labs will allow students to design basic ground-up "intelligent audio" systems, use existing MIR toolboxes, applications, and complex systems.

Knowledge of basic digital audio principles, and familiarity with basic programming (Matlab) will be useful. Students are highly encouraged to bring their own audio source material for course demos.