MIR workshop 2010

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(FOR THE LATEST MIR WORKSHOP - July 2011 - PLEASE VISIT HERE)
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* 9-5 PM.  July 12-16, 2010
* 9-5 PM.  July 12-16, 2010
* Instructors: [http://www.imagine-research.com/ Jay LeBoeuf] and [http://www.cs.princeton.edu/~fiebrink/ Rebecca Fiebrink]
* Instructors: [http://www.imagine-research.com/ Jay LeBoeuf] and [http://www.cs.princeton.edu/~fiebrink/ Rebecca Fiebrink]
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* Participants:  
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* Participants:
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= [https://ccrma.stanford.edu/wiki/MIR_workshop_2011 FOR THE LATEST MIR WORKSHOP - July 2011 - PLEASE VISIT HERE ] =
== Abstract ==  
== Abstract ==  
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<br><u>Lab 4: </u>
<br><u>Lab 4: </u>
* [http://ccrma.stanford.edu/workshops/mir2010/Lab4_2010.pdf GMM Lab]
* [http://ccrma.stanford.edu/workshops/mir2010/Lab4_2010.pdf GMM Lab]
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* [https://ccrma.stanford.edu/workshops/mir2010/weka_lab1.pdf Wekinator Lab]
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* [https://ccrma.stanford.edu/workshops/mir2010/Lab4Wekinator.pdf Wekinator Lab]
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<br><u>Day 5:</u> [http://ccrma.stanford.edu/workshops/mir2009/CCRMA_MIR_2009_Lecture_5.pdf Lecture 5 Slides]
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<br><u>Day 5:</u> [http://ccrma.stanford.edu/workshops/mir2010/CCRMA_MIR_2010_Lecture5.pdf Lecture 5 Slides - Part 1]
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* Building and evaluating systems - assembling testing and training sets - J
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* Building and evaluating systems - assembling testing and training sets  
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* Classification: SVM - J
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* Classification: SVM  
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* IR Evaluation Metrics (precision, recall, f-measure, AROC,...)  -R
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* IR Evaluation Metrics (precision, recall, f-measure, AROC,...)   
** [http://ccrma.stanford.edu/workshops/mir2009/references/recall_precision.pdf Recall-Precision]
** [http://ccrma.stanford.edu/workshops/mir2009/references/recall_precision.pdf Recall-Precision]
** [http://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf ROC Analysis]
** [http://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf ROC Analysis]
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* Practical tips & tricks                -R/J
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* Practical tips & tricks                 
** PCA & LDA
** PCA & LDA
* Student-Selected Optional Lecture Topics:  
* Student-Selected Optional Lecture Topics:  
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<br><u>Lab 5</u>
<br><u>Lab 5</u>
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* [http://ccrma.stanford.edu/workshops/mir2009/Lab%205%20-%20SVMs.pdf Building classifiers with SVMs]  
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* [http://ccrma.stanford.edu/workshops/mir2010/Lab5_2010.pdf Building classifiers with SVMs]  
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* miniMIREX
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* [http://ccrma.stanford.edu/workshops/mir2010/miniMirex.pdf miniMIREX]
<br><u>Bonus Lab material</u>
<br><u>Bonus Lab material</u>
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== Audio Source Material ==
== Audio Source Material ==
OLPC Sound Sample Archive (8.5 GB) [http://wiki.laptop.org/go/Sound_samples]
OLPC Sound Sample Archive (8.5 GB) [http://wiki.laptop.org/go/Sound_samples]
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http://www.tsi.telecom-paristech.fr/aao/en/category/database/
RWC Music Database (n DVDs) [available in Stanford Music library]
RWC Music Database (n DVDs) [available in Stanford Music library]

Revision as of 20:56, 29 July 2011

Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval

Contents

Logistics

Workshop Title: "Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval"


FOR THE LATEST MIR WORKSHOP - July 2011 - PLEASE VISIT HERE

Abstract

Description: How would you "Google for audio", provide music recommendations based your MP3 files, or have a computer "listen" and understand what you are playing?

This workshop will teach the underlying ideas, approaches, 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 reduce 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 is intended for: 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 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. Knowledge of basic digital audio principles is required. Familiarity with Matlab is desired. Students are highly encouraged to bring their own audio source material for course labs and demonstrations.

Workshop structure: The workshop will consist of half-day lectures, half-day supervised lab sessions, 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.

software, libraries, examples

Applications & Environments

Machine Learning Libraries & Toolboxes

Optional Toolboxes

Lectures & Labs


Day 1: Lecture 1 Slides

  • CCRMA Introduction - (Carr/Sasha)
  • Interest in a CCRMA Tour?
  • Introduction to MIR (What is MIR? Why are people interested? Commercial Applications of MIR)
  • A brief history of MIR
  • Overview of a basic MIR system architecture
  • Timing and Segmentation: Frames, Onsets
  • Features: ZCR, Spectral moments
  • Demo: Using simple heuristics and thresholds (i.e. "Why do we need machine learning?")
  • Classification: Instance-based classifiers (k-NN)
  • Features: Scaling of feature data


Lab 1: Lab 1 -"Manipulating audio slices"
Students who need a personal tutorial of Matlab or audio signal processing will split off and received small group assistance to bring them up to speed.


Day 2: Lecture 2 Slides

  • Features: Additional spectral features (Spread, Flatness, temporal features, octave bands, spectral envelopes, MFCCs)
  • Decision boundaries, decision stumps, and decision trees
  • AdaBoost
  • Classifier evaluation (Cross-validation, training and test sets)


Lab 2: [Lab 2

  • If you finish early, see the "bonus labs" section below.


Day 3: Lecture 3 Slides - Part 1 Lecture 3 Slides - Part 2


Lab 3: Clustering lab with MFCCs

  • If you finish early, see the "bonus labs" section below.


Day 4: Lecture 4 Slides - Part 1


Lab 4:


Day 5: Lecture 5 Slides - Part 1


Lab 5


Bonus Lab material

Supplemental papers and information for the lectures...

Explanations, tutorials, code demos, recommended papers here - for each topic....

Past CCRMA MIR Workshops and lectures

References for additional info

Recommended books:

  • Data Mining: Practical Machine Learning Tools and Techniques, Second Edition by Ian H. Witten , Eibe Frank (includes software)
  • Netlab by Ian T. Nabney (includes software)
  • Signal Processing Methods for Music Transcription, Klapuri, A. and Davy, M. (Editors)
  • Computational Auditory Scene Analysis: Principles, Algorithms, and Applications, DeLiang Wang (Editor), Guy J. Brown (Editor)
  • Speech and Audio Signal Processing:Processing and perception of speech and music Ben Gold & Nelson Morgan, Wiley 2000

Prerequisite / background material:

Papers:

Other books (not necessary reviewed by the instructors yet):

  • Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop
  • Neural Networks for Pattern Recognition, Christopher M. Bishop, Oxford University Press, 1995.
  • Pattern Classification, 2nd edition, R Duda, P Hart and D Stork, Wiley Interscience, 2001.
  • "Artificial Intelligence: A Modern Approach" Second Edition, Russell R & Norvig P, Prentice Hall, 2003.
  • Machine Learning, Tom Mitchell, McGraw Hill, 1997.

Interesting Links:

Audio Source Material

OLPC Sound Sample Archive (8.5 GB) [1]

http://www.tsi.telecom-paristech.fr/aao/en/category/database/

RWC Music Database (n DVDs) [available in Stanford Music library]

RWC - Sound Instruments Table of Contents

http://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html

Univ or Iowa Music Instrument Samples

See longer list on resources page: https://ccrma.stanford.edu/wiki/MIR_workshop_2008_notes#Research_Databases_.2F_Collections_of_Ground_truth_data_and_copyright-cleared_music

MATLAB Utility Scripts

http://ccrma.stanford.edu/~kglee/kaist_summer2008_special_lecture/

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