View source for MIR workshop 2010

<b>Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval</b>

== Logistics ==
Workshop Title: '''"Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval"
'''
* 9-5 PM.  July 12-16, 2010
* Instructors: [http://www.imagine-research.com/ Jay LeBoeuf] and [http://www.cs.princeton.edu/~fiebrink/ Rebecca Fiebrink]
* Participants: 

== 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
* [http://www.mathworks.com/products/matlab/ MATLAB]
* [http://www.cs.waikato.ac.nz/ml/weka/ Weka Machine Learning and Data Mining Toolbox (Standalone app / Java)] 

Machine Learning Libraries & Toolboxes
* [http://www.ncrg.aston.ac.uk/netlab/ Netlab Pattern Recognition and Clustering Toolbox (Matlab)]
* [http://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab libsvm SVM toolbox (Matlab)] 
* [http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox/Download/fg_base_view MIR Toolboxes (Matlab)]
* [http://cosmal.ucsd.edu/cal/projects/CATbox/catbox.htm UCSD CatBox]
Optional Toolboxes
* [http://www.ofai.at/~elias.pampalk/ma/ MA Toolbox]
* [http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/miditoolbox MIDI Toolbox] 
* [see also below references]
* [http://marsyas.sness.net/ Marsyas]
* CLAM
* Genetic Algorithm: http://www.ise.ncsu.edu/mirage/GAToolBox/gaot/
* Spider http://www.kyb.tuebingen.mpg.de/bs/people/spider/
* HTK http://htk.eng.cam.ac.uk/

== Lectures & Labs ==
<br><u>Day 1:</u> [https://ccrma.stanford.edu/workshops/mir2010/CCRMA_MIR_2010_Lecture_1.pdf 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 
** See also http://www.ismir.net/texts/Byrd02.html
* 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  

<br><u>Lab 1:</u> [http://ccrma.stanford.edu/workshops/mir2010/Lab1_2010.pdf Lab 1 -"Manipulating audio slices"] <br>
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.
* Background for students needing a refresher:
** [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/2_fft.pdf Fundamentals of Digital Audio Signal Processing (lecture slides from Juan Bello)]
** [http://ccrma.stanford.edu/workshops/mir2009/Lab0/lab0.html Fundamentals of Matlab]
** [http://ccrma.stanford.edu/workshops/mir2009/Lab1/lab1.html Fundamentals of Digital Audio Signal Processing (FFT, STFT, Windowing, Zero-padding, 2-D Time-frequency representation)]
* REMINDER: Save all your work, because you may want to build on it in subsequent labs.

<br><u>Day 2:</u> [http://ccrma.stanford.edu/workshops/mir2010/CCRMA_MIR_2010_Lecture_2.pdf Lecture 2 Slides]
* Features: Additional spectral features (Spread, Flatness, temporal features, octave bands, spectral envelopes, MFCCs)  -J
** Chroma - R (with Jay's slides)
** [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/3_feature.pdf Spectral Features (lecture slides from Juan Bello)]
* Decision boundaries, decision stumps, and decision trees -R (after break)
* AdaBoost -R
* Classifier evaluation (Cross-validation, training and test sets) - J


<br><u>Lab 2:</u> [[http://ccrma.stanford.edu/workshops/mir2010/Lab2_2010.pdf Lab 2]
* If you finish early, see the "bonus labs" section below.

<br><u>Day 3:</u> [http://ccrma.stanford.edu/workshops/mir2009/CCRMA_MIR_2009_Lecture_3.pdf Lecture 3 Slides]
* Classification: Unsupervised classification (k-means)  -J
* Probabilistic and graphical models -R
** See http://soundlab.cs.princeton.edu/publications/2009_ismir_cba.pdf for Hoffman's example paper discussed in lecture
* Ask for grab-bag votes

<br><u>Lab 3:</u> [http://ccrma.stanford.edu/workshops/mir2009/Lab%203%20-%20Cluster%20Lab.pdf Clustering lab with MFCCs]

<br><u>Day 4:</u> [http://ccrma.stanford.edu/workshops/mir2009/CCRMA_MIR_2009_Lecture_4.pdf Lecture 4 Slides]
* Obtaining MIR Data: Social mining and MIR games, datasets - R
* Classification: GMM   -J
* Classification examples:  -J
** Speech/Music Discrimination
** Genre Classification
* Overview of Weka & the Wekinator -R

<br><u>Lab 4: </u>
* [http://ccrma.stanford.edu/workshops/mir2009/Lab%204%20-%20Gaussian%20Mixture%20Models.pdf GMM Lab]
* The Wekinator

<br><u>Day 5:</u> [http://ccrma.stanford.edu/workshops/mir2009/CCRMA_MIR_2009_Lecture_5.pdf Lecture 5 Slides]
* Building and evaluating systems - assembling testing and training sets - J
* Classification: SVM - J
* IR Evaluation Metrics (precision, recall, f-measure, AROC,...)   -R
** [http://ccrma.stanford.edu/workshops/mir2009/references/recall_precision.pdf Recall-Precision]
** [http://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf ROC Analysis]
* Practical tips & tricks                -R/J
** PCA & LDA
* Student-Selected Optional Lecture Topics: 
** Playlist generation
** Visualization 
** Similarity and recommendation
** Feature selection
** Fingerprinting
*** See also: http://www.ee.columbia.edu/~dpwe/e4896/practicals.html#20100421
** MIREX
** Structural analysis
** [https://ccrma.stanford.edu/workshops/mir2009/Lab4/lab4.m Similarity Matrix]

<br><u>Lab 5</u>
* [http://ccrma.stanford.edu/workshops/mir2009/Lab%205%20-%20SVMs.pdf Building classifiers with SVMs] 
* miniMIREX

<br><u>Bonus Lab material</u>
* [http://ccrma.stanford.edu/workshops/mir2010/weka_lab1.pdf Getting started with Weka]
* Harmony Analysis Slides / Labs
** [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/6_harmony.pdf Harmony Analysis (lecture slides from Juan Bello)]
** [http://ccrma.stanford.edu/workshops/mir2009/references/klee-ieee-taslp08-print.pdf Chord recognition using HMMs (Kyogu Lee)]
** [http://ccrma.stanford.edu/workshops/mir2009/references/klee-lncs08.pdf Genre-specific chord recognition using HMMs (Kyogu Lee)]
** [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.tgz Lab - download lab3.tgz]
** [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.html Lab - Key estimation, chord recognition]

== Supplemental papers and information for the lectures...==
[http://cm-wiki.stanford.edu/wiki/MIR_workshop_2008_notes Explanations, tutorials, code demos, recommended papers here - for each topic....]

== Past CCRMA MIR Workshops and lectures== 
* [http://ccrma.stanford.edu/wiki/MIR_workshop_2009 CCRMA MIR Summer Workshop 2009]
* [http://cm-wiki.stanford.edu/wiki/MIR_workshop_2008 CCRMA MIR Summer Workshop 2008]

== 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: 
* http://140.114.76.148/jang/books/audioSignalProcessing/
* [http://ccrma.stanford.edu/workshops/mir2008/learnmatlab_sp3.pdf The Mathworks' Matlab Tutorial]
* [http://ismir2007.ismir.net/proceedings/ISMIR2007_tutorial_Lartillot.pdf ISMIR2007 MIR Toolbox Tutorial]

Papers:
* Check out the references listed at the end of the Klapuri & Davy book
* Check out Papers listed on Pg 136-7 of  MIR Toolbox: http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox/userguide1.1

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: 
* http://www.ifs.tuwien.ac.at/mir/howtos.html
* http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials
* http://www.music-ir.org/evaluation/tools.html
* http://140.114.76.148/jang/matlab/toolbox/
* http://htk.eng.cam.ac.uk/

== Audio Source Material ==
OLPC Sound Sample Archive (8.5 GB) [http://wiki.laptop.org/go/Sound_samples]

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

[http://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html RWC - Sound Instruments Table of Contents]

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

[http://theremin.music.uiowa.edu/MIS.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/~mw/ Mike's scripts] 

* [[Reading MP3 Files]]
* [[Low-Pass Filter]]
* Steve Tjoa: [http://ccrma.stanford.edu/~kiemyang/software Matlab code] (updated July 9, 2009)

[[Category: Workshops]]

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