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<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'''
* Monday, June 25, through Friday, June 29, 2012. 9:30 AM (not 9 AM) to 5 PM every day.
* Location: The Knoll, CCRMA, Stanford University. http://goo.gl/maps/nNKx
* Instructors:
** Jay LeBoeuf, [http://www.izotope.com iZotope, Inc.],  [http://www.imagine-research.com Imagine Research, Inc.]
** Steve Tjoa,  [http://www.izotope.com iZotope, Inc.]
** Leigh Smith,  [http://www.izotope.com iZotope, Inc.]
* Participants:
== Abstract ==
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.
== Schedule: Lectures & Labs ==
=== Day 1: Introduction to MIR, Signal Analysis and Feature Extraction ===
Presenters: Jay LeBoeuf, Leigh Smith
<br><u>Day 1: Part 1</u> [http://ccrma.stanford.edu/workshops/mir2012/CCRMA2012day1.pdf Lecture 1 Part 1 Slides]
* Introductions 
* CCRMA Introduction - (Carr/Sasha).  CCRMA Tour.
* Introduction to MIR (What is MIR? Why are people interested? Commercial Applications of MIR) 
* Overview of a basic MIR system architecture   
* Timing and Segmentation: Frames, Onsets     
* Features: ZCR, Spectral moments; Scaling of feature data 
* Demo: Using simple heuristics and thresholds (i.e. "Why do we need machine learning?")
* Classification: Instance-based classifiers (k-NN) 
* Information Retrieval Basics (Part 1)
** Classifier evaluation (Cross-validation, training and test sets)
<br><u>Day 1: Part 2</u> [http://ccrma.stanford.edu/workshops/mir2012/CCRMA_MIR2012_FeatureExtraction.pdf Lecture 2 Slides]
* Overview: Signal Analysis and Feature Extraction for MIR Applications (Historical: http://quod.lib.umich.edu/cgi/p/pod/dod-idx?c=icmc;idno=bbp2372.1999.356)
* MIR Application Design
** Audio input, analysis
** Statistical/perceptual processing
** Data storage
** Post-processing
* Windowed Feature Extraction
** I/O and analysis loops
* Feature-vector design (Overview: http://www.create.ucsb.edu/~stp/PostScript/PopeHolmKouznetsov_icmc2.pdf)
** Kinds/Domains of Features
** Application Requirements (labeling, segmentation, etc.)
* Time-domain features (MPEG-7 Audio book ref)
** RMS, Peak, LP/HP RMS, Dynamic range, ZCR
* Frequency-domain features
** Spectrum, Spectral bins
** Spectral measures (statistical moments)
** Pitch-estimation and tracking
** MFCCs
* Spatial-domain features
** M/S Encoding, Surround-sound Processing Frequency-dependent spatial separation, LCR sources
* Other Feature domains
** Wavelets, LPC
* Application: Instrument recognition and drum transcription / Using simple heuristics and thresholds (i.e. "Why do we need machine learning?")
<br><u>Lab 1:</u> <br>
* [https://ccrma.stanford.edu/workshops/mir2011/Lab_1_2012.pdf Lab 1 - Basic Feature Extraction and Classification] <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.
=== Day 2: Beat-finding and Rhythm Analysis ===
Presenter: Leigh Smith
[http://ccrma.stanford.edu/workshops/mir2012/CCRMA_MIR2012_Beat.pdf Lecture 3 Slides]
[http://ccrma.stanford.edu/workshops/mir2011/BeatReferences.pdf A list of beat tracking references cited]
* Onset-detection: Many Techniques
** Time-domain differences
** Spectral-domain differences
** Perceptual data-warping
** Adaptive onset detection
* Beat-finding and Tempo Derivation
** IOIs and Beat Regularity, Rubato
*** Tatum, Tactus and Meter levels
*** Tempo estimation
** Onset-detection vs Beat-detection
*** The Onset Detection Function
** Approaches to beat tracking & Meter estimation
*** Autocorrelation
*** Beat Spectrum measures
*** Multi-resolution (Wavelet)
** Beat Histograms
** Fluctuation Patterns
** Joint estimation of downbeat and chord change
<br><u>Lab 2:</u>
* [http://ccrma.stanford.edu/workshops/mir2012/FeatureDetection_lab2_2012.pdf Feature extraction and cross-validation in MATLAB]
* Down-loads
**  [https://ccrma.stanford.edu/workshops/mir2011/MAT240F-Reader.zip UCSB MAT 240F Reader]
**  [https://ccrma.stanford.edu/workshops/mir2011/MAT240F-Code.zip UCSB MAT 240F Code]
**  [https://ccrma.stanford.edu/workshops/mir2011/MAT240F-Sounds.zip UCSB MAT 240F Sounds]
**  [https://ccrma.stanford.edu/workshops/mir2012/ODF.zip Onset Detection Function example code in Octave/Matlab]
=== Day 3: Music Information Retrieval in Polyphonic Mixtures ===
Presenter: Steve Tjoa
'''Lecture/Lab 3'''
* [http://ccrma.stanford.edu/workshops/mir2012/tjoa20120627ccrma.pdf Lecture and Lab 3 Slides, Steve Tjoa, 2012]
* Music Transcription and Source Separation
* Nonnegative Matrix Factorization
* Sparse Coding
* Locality Sensitive Hashing
=== Day 4: Pitch, Chroma, More Classification ===
Presenters: Steve Tjoa, Oscar Celma (Gracenote)
'''Lecture 4'''
* [http://ccrma.stanford.edu/workshops/mir2011/ccrma_2011_pitch_reps.pdf Pitch Representation Slides (.pdf), George Tzanetakis]
* Features:
** Monophonic Pitch Detection
** Polyphonic Pitch Detection
** Pitch representations (Tuning Histograms, Pitch and Pitch Class Profiles, Chroma)
* Analysis:
** Dynamic Time Warping
** Hidden Markov Models
** Harmonic Analysis/Chord and Key Detection
* Applications
** Audio-Score Alignment
** Cover Song Detection
** Query-by-humming
** Music Transcription
* Music Recomendation
** Overview of music recommendation. What's hard about it.
** Some statistics and observations about the music industry and the need for recomendation.
** Point-Counterpoint: Should we bother with content-based analysis.
'''Lab 4'''
* [http://ccrma.stanford.edu/workshops/mir2012/2012-ClusterLab.pdf K-Means]
* [http://ccrma.stanford.edu/workshops/mir2012/Lab5-SVMs.pdf SVM]
=== Day 5: Michael Mandel, Jay LeBoeuf, Steve Tjoa, Leigh Smith  ===
Guest Lecture: Autotagging, Michael Mandel
* [http://majorminer.org/info/intro Major Miner Game]
* [http://musicallyintelligent.com/ Musically Intelligent Machines]
* [https://ccrma.stanford.edu/workshops/mir2012/CCRMA%202012%20day1%20v5.pdf Day 5 Slides (.pdf)]
** 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/ROCintro.pdf ROC Analysis]
*  Autotagging
** Features for autotagging (some of this will be review, given days 1 through 4.)
** Demos of clustering for different types of acoustic features.
** Training Data.
** Classifiers (focus on AdaBoost)
** Feature selection.
** Evaluation with lots of examples.
* Time permitting:
** Advanced features
** Sparse coding
** Using musical structure.
'''Lab 5'''
Below are some links to functions and matlab code for key estimation, chord recognition, and GMMs: 
** [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]
** [http://ccrma.stanford.edu/workshops/mir2010/Lab4_2010.pdf GMM Lab]
<br><u>Bonus Lab material</u>
* Insert your bonus lab materials here...
* 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]
* [http://ccrma.stanford.edu/workshops/mir2011/weka_lab1.pdf Getting started with Weka]
* [https://ccrma.stanford.edu/workshops/mir2011/Wekinator_lab_2011.pdf Wekinator Lab]
* Overview of Weka & the Wekinator
** [http://www.cs.waikato.ac.nz/ml/weka/ Weka home]
** [http://code.google.com/p/wekinator/ Wekinator on Google code] and [http://wiki.cs.princeton.edu/index.php/ChucK/Wekinator/Instructions instructions]
* A brief history of MIR
** See also http://www.ismir.net/texts/Byrd02.html
* Notes
** CAL500 decoding
for i in *.mp3; do echo $i; afconvert -d BEI16@44100 -f AIFF "$i"; done
* Extract CAL 500 per-song features to .mat or .csv using features from today.  This will be used on lab for Friday. Copy it from the folder ccrma-gate.stanford.edu:/usr/ccrma/workshops/mir2011/cal500.tar (beware it's a 2Gb .tar file!) or grab the AIFF versions from ccrma-gate.stanford.edu:/usr/ccrma/workshops/mir2011/cal500_aiffs.tar (that's 16 GB)
== 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]
* 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/
== 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....]
* [http://ccrma.stanford.edu/workshops/mir2011/BeatReferences.pdf A list of beat tracking references cited]
== Past CCRMA MIR Workshops and lectures==
* [http://ccrma.stanford.edu/wiki/MIR_workshop_2011 CCRMA MIR Summer Workshop 2011]
* [http://ccrma.stanford.edu/wiki/MIR_workshop_2010 CCRMA MIR Summer Workshop 2010]
* [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://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]
* ISMIR 2011 Proceedings: http://ismir2011.ismir.net/program.html
* 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:
* 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://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://theremin.music.uiowa.edu/MIS.html Univ or Iowa Music Instrument Samples ]
== 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]]

Revision as of 15:43, 25 June 2013