Difference between revisions of "MIR workshop 2008"

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== Abstract ==  
 
== Abstract ==  
  
−
Music Information Retrieval (MIR) is a highly-interdisciplinary field bridging the domains of digital audio signal processing, pattern recognition, software system design, and machine learning.  Simply put, MIR algorithms allow a computer to “listen” and “understand or make sense of” audio data, such as MP3's in a personal music collection, live streaming audio, or gigabytes of sound effects, in an effort to close the semantic gap between high-level musical information and low-level audio data.  In the same way that listeners can recognize the characteristics of sound and music – tempo, key, chord progressions, genre, or song structure – MIR algorithms are capable of recognizing and extracting this information, enabling systems to perform extensive sorting, searching, music recommendation, metadata generation, transcription, and even aiding/generating real-time performance.
+
This workshop will teach the underlying technologies and practical design of intelligent audio systems using Music Information Retrieval (MIR) algorithms.   
  
−
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 the understanding and usage of complex algorithms approachable.    
+
MIR is a highly-interdisciplinary field bridging the domains of digital audio signal processing, pattern recognition, software system design, and machine learning.  Simply put, MIR algorithms allow a computer to “listen” and “understand or make sense of” audio data, such as MP3s in a personal music collection, live streaming audio, or gigabytes of sound effects, in an effort to close the semantic gap between high-level musical information and low-level audio data.  In the same way that listeners can recognize the characteristics of sound and music – tempo, key, chord progressions, genre, or song structure – MIR algorithms are capable of recognizing and extracting this information, enabling systems to perform extensive sorting, searching, music recommendation, metadata generation, transcription, and even aiding/generating real-time performance.
  
−
The workshop will consist of half-day lectures, half-day supervised lab sessions, and classroom exercises and discussions.
+
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.  Lectures will cover topics such as low-level feature extraction, generation of higher-level features such as onset timings and chord estimations, audio similarity clustering, search, and retrieval techniques, and design and evaluation of machine classification systems.  The presentations will be applied, multimedia-rich, overview of the building blocks of modern MIR systems.  Our goal is to make the understanding and application of highly-interdisciplinary technologies and complex algorithms approachable.    
  
−
Labs will allow students to design basic ground-up "intelligent audio" systems, use existing MIR toolboxes, programming environments, applications, and complex systems.
+
The workshop will consist of half-day lectures, half-day supervised lab sessions, classroom exercises, demonstrations, and discussions.
  
−
Knowledge of basic digital audio principles, and familiarity with basic programming (Matlab, C/C++, and/or ChucK) will be useful.  
+
Labs will allow students to design basic ground-up "intelligent audio systems", leveraging existing MIR toolboxes, programming environments, and applications.  Labs will include creation and evaluation of basic instrument recognition, transcription, and real-time audio analysis systems. 
−
Students are highly encouraged to bring their own audio source material for course demos.
+
 
 +
Knowledge of basic digital audio principles and familiarity with basic programming (Matlab, C/C++, and/or ChucK) will be useful.  
 +
Students are highly encouraged to bring their own audio source material for course labs and demonstrations.
  
 
== References for additional info ==  
 
== References for additional info ==  

Revision as of 10:26, 20 March 2008

CCRMA Workshop: Music Information Retrieval

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

logistics


potential workshop titles

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

== workshop outline == Introduction to Capabilities of MIR

Survey of the field, real-world applications, MIR research, and challenges

  • Current commercial applications
    • Music Recommender Systems
    • Playlisting systems
    • DJ systems
    • Music Transcription
    • DAW technologies
    • Band in a box
  • Academic MIR research projects
    • MARSYAS
    • CLAM
    • IMIRSEL
    • Ongoing work and projects at McGill / UCSD / Columbia / Princeton / Stanford / UK / beyond


Signal Processing Basics (if necessary)

Feature Extraction

  • Low Level Features
    • "Classic" Spectral features (Centroid, Flux, RMS, Rolloff, Flatness, Kurtosis)
    • Zero Crossing
    • Beat Histogram (?)
    • Spectral Bands / Filters
    • MFCC, LPC, (source-filter modeling)
    • MPEG-7
  • Higher-level features
    • Chroma features
    • Key Estimation
    • Chord Estimation
    • 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

  • CASA 101
  • Structural Analysis and Segmentation

Classification Algorithms

  • k-NN
  • SVM
  • HMM
  • Neural Nets

Classification

  • concept and design
  • genre-classification
  • similarity retrieval
  • instrument/speaker/source identification

Evaluation Methodology

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

Lab Exercises

  • Feature extraction from audio
  • Classification tasks
  • Building an Instrument Identifier Tool using source audio material
  • Organization of data sets and Evaluating system accuracy
  • Speaker change detection
  • Clustering Techniques Demo: Song Segmentation, Drum Transcription
  • Prototyping real-time MIR algorithms and systems with ChucK/UAna

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)
  • MA, MIDI, and MIR Toolboxes (Matlab)
  • [see also below references]

Abstract

This workshop will teach the underlying technologies and practical design of intelligent audio systems using Music Information Retrieval (MIR) algorithms.

MIR is a highly-interdisciplinary field bridging the domains of digital audio signal processing, pattern recognition, software system design, and machine learning. Simply put, MIR algorithms allow a computer to “listen” and “understand or make sense of” audio data, such as MP3s in a personal music collection, live streaming audio, or gigabytes of sound effects, in an effort to close the semantic gap between high-level musical information and low-level audio data. In the same way that listeners can recognize the characteristics of sound and music – tempo, key, chord progressions, genre, or song structure – MIR algorithms are capable of recognizing and extracting this information, enabling systems to perform extensive sorting, searching, music recommendation, metadata generation, transcription, and even aiding/generating real-time performance.

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. Lectures will cover topics such as low-level feature extraction, generation of higher-level features such as onset timings and chord estimations, audio similarity clustering, search, and retrieval techniques, and design and evaluation of machine classification systems. The presentations will be applied, multimedia-rich, overview of the building blocks of modern MIR systems. Our goal is to make the understanding and application of highly-interdisciplinary technologies and complex algorithms approachable.

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

Labs will allow students to design basic ground-up "intelligent audio systems", leveraging existing MIR toolboxes, programming environments, and applications. Labs will include creation and evaluation of basic instrument recognition, transcription, and real-time audio analysis systems.

Knowledge of basic digital audio principles and familiarity with basic programming (Matlab, C/C++, and/or ChucK) will be useful. Students are highly encouraged to bring their own audio source material for course labs and demonstrations.

References for additional info

Tools:

Toolboxes to explore:

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