Latest revision as of 14:23, 26 June 2013

Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval

Logistics

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

9-5 PM. July 12-16, 2010
Instructors: Jay LeBoeuf and Rebecca Fiebrink
Participants:

FOR THE LATEST MIR WORKSHOP - 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

MA Toolbox
MIDI Toolbox
[see also below references]
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

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
- 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

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.

Background for students needing a refresher:
REMINDER: Save all your work, because you may want to build on it in subsequent labs.

Day 2: Lecture 2 Slides

Features: Additional spectral features (Spread, Flatness, temporal features, octave bands, spectral envelopes, MFCCs)
- Chroma
- Spectral Features (lecture slides from Juan Bello)
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

Classification: Unsupervised classification (k-means)
Probabilistic and graphical models
- See http://soundlab.cs.princeton.edu/publications/2009_ismir_cba.pdf for Hoffman's example paper discussed in lecture
Ask for grab-bag votes

Lab 3: Clustering lab with MFCCs

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

Day 4: Lecture 4 Slides - Part 1

Obtaining MIR Data: Social mining and MIR games, datasets
- Data resources list
- Also see https://ccrma.stanford.edu/wiki/MIR_workshop_2010#Audio_Source_Material and https://ccrma.stanford.edu/wiki/MIR_workshop_2008_notes#Research_Databases_.2F_Collections_of_Ground_truth_data_and_copyright-cleared_music
Classification: GMM
Classification examples:
- Speaker Identification
- Genre Classification
Overview of Weka & the Wekinator
- Weka home
- Wekinator on Google code and instructions

Lab 4:

Day 5: Lecture 5 Slides - Part 1

Building and evaluating systems - assembling testing and training sets
Classification: SVM
IR Evaluation Metrics (precision, recall, f-measure, AROC,...)
- Recall-Precision
- ROC Analysis
Practical tips & tricks
- 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
- Similarity Matrix

Lab 5

Bonus Lab material

Getting started with Weka
Harmony Analysis Slides / Labs

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:

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:

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

Mike's scripts

Reading MP3 Files
Low-Pass Filter
Steve Tjoa: Matlab code (updated July 9, 2009)

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

Difference between revisions of "MIR workshop 2010"

Latest revision as of 14:23, 26 June 2013

Contents

Logistics

FOR THE LATEST MIR WORKSHOP - PLEASE VISIT HERE

Abstract

software, libraries, examples

Lectures & Labs

Supplemental papers and information for the lectures...

Past CCRMA MIR Workshops and lectures

References for additional info

Audio Source Material

MATLAB Utility Scripts

Navigation menu

Views

Personal tools

Navigation

Search

Tools

@@ Line 1: / Line 1: @@
-= Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval =
+<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, 2009
+* 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:
+= [https://ccrma.stanford.edu/wiki/MIR_workshop_2013 FOR THE LATEST MIR WORKSHOP - PLEASE VISIT HERE ] =
 == Abstract ==
@@ Line 16: / Line 19: @@
 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.
-== Workshop syllabus ==
-* Administration
-** Introductions
-** CCRMA Overview
-* Introduction to Capabilities and Applications of MIR
-** Why MIR?
-** Overview of potential research and commercial applications
-** Basic System Overview and Architecture
-=== Timing and Segmentation ===
-* Frames and Windows
-* Onset Detection
-* Beat & Tempo Extraction
-=== Feature Extraction ===
-* Low Level Features
-** Zero Crossing
-** Temporal centroid, Log Attack time, Attack slope
-** Spectral features (Centroid, Flux, RMS, Rolloff, Flatness, Kurtosis, Brightness)
-** Spectral bands
-** Log spectrogram
-** Chroma bins
-** MFCC
-* Higher-level features
-** Tempo/BPM
-** Key Estimation
-** Chord Estimation
-** Genre (genre, artist ID, similarity)
-=== Analysis / Decision Making ===
-* Classification
-** Heuristic Analysis
-** k-NN
-** SVM
-* Clustering and probability density models
-** k-Means
-** Clustering
-** GMM
-=== Model / Data Preparation Techniques ===
-* Data Preparation
-** Scaling data
-* Model organization
-** concept and design
-** Data set construction and organization
-=== Evaluation Methodology ===
-* Feature selection
-* Cross Validation
-* Information Retrieval metrics (precision, recall, F-Measure)
-Plus guest lectures/visits from academic experts and real-world folks.
 == software, libraries, examples ==
@@ Line 92: / Line 41: @@
 == Lectures & Labs ==
-<br><u>Day 1:</u> [http://ccrma.stanford.edu/workshops/mir2009/CCRMA_MIR_2009_Lecture_1.pdf Lecture 1 Slides]
+<br><u>Day 1:</u> [https://ccrma.stanford.edu/workshops/mir2010/CCRMA_MIR_2010_Lecture_1.pdf Lecture 1 Slides]
 * CCRMA Introduction - (Carr/Sasha)
-* Introduction to MIR (What is MIR? Why are people interested? Commercial Applications of MIR) -J
+* Interest in a CCRMA Tour?
-* Overview of a basic MIR system architecture    -J
+* Introduction to MIR (What is MIR? Why are people interested? Commercial Applications of MIR)
-* Timing and Segmentation: Frames, Onsets       -J
+* A brief history of MIR
-* Features: ZCR, Spectral moments               -J
+** See also http://www.ismir.net/texts/Byrd02.html
-* Demo: Using simple heuristics and thresholds (i.e. "Why do we need machine learning?") -J
+* Overview of a basic MIR system architecture
-* Classification: Instance-based classifiers (k-NN)  -R
+* Timing and Segmentation: Frames, Onsets
-* Features: Scaling of feature data  -R
+* Features: ZCR, Spectral moments
-* Obtaining MIR Data: MIR Data sets - R
+* Demo: Using simple heuristics and thresholds (i.e. "Why do we need machine learning?")
-* Other?
+* Classification: Instance-based classifiers (k-NN)
-*
+* Features: Scaling of feature data
-* [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/2_fft.pdf Fundamentals of Digital Audio Signal Processing (lecture slides from Juan Bello)]
-<br><u>Lab 1:</u> [http://ccrma.stanford.edu/workshops/mir2009/Lab%201%20-%20Playing%20with%20audio%20slices.pdf Lab 1 -"Manipulating audio slices"] <br>
+<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/mir2009/CCRMA_MIR_2009_Lecture_2.pdf Lecture 2 Slides]
+<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)  -J
+* Features: Additional spectral features (Spread, Flatness, temporal features, octave bands, spectral envelopes, MFCCs)
+** Chroma
 ** [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/3_feature.pdf Spectral Features (lecture slides from Juan Bello)]
-* Introduction to Bayesian Techniques - R
+* Decision boundaries, decision stumps, and decision trees
-* IR Methodologies (Cross-validation, training and test sets)            - J
+* AdaBoost
-* Introduction to the [http://wekinator.cs.princeton.edu/ Wekinator] - R
+* Classifier evaluation (Cross-validation, training and test sets)
-<br><u>Lab 2:</u> [[http://ccrma.stanford.edu/workshops/mir2009/Lab%202%20-%20My%20first%20audio%20classifier.pdf Lab 2- Build simple classifiers using new features]
+<br><u>Lab 2:</u> [[http://ccrma.stanford.edu/workshops/mir2010/Lab2_2010.pdf Lab 2]
-* [http://ccrma.stanford.edu/workshops/mir2009/Lab%205b%20Cross%20Validation%20Code.pdf Cross Validation and Accuracy measures]
+* If you finish early, see the "bonus labs" section below.
-* Introduction to the Wekinator-The-Lab
-<br><u>Day 3:</u> [http://ccrma.stanford.edu/workshops/mir2009/CCRMA_MIR_2009_Lecture_3.pdf Lecture 3 Slides]
+<br><u>Day 3:</u>
-* Features: Temporal features; Octave-bands  -J
+[http://ccrma.stanford.edu/workshops/mir2010/CCRMA_MIR_2010_Lecture_3.pdf Lecture 3 Slides - Part 1]
-* Features: Spectral Envelopes, MFCCs  -J
+[http://ccrma.stanford.edu/workshops/mir2010/GraphicalModelsIntro.pdf Lecture 3 Slides - Part 2 ]
-* Classification: Unsupervised classification (k-means)  -J
+* Classification: Unsupervised classification (k-means)
-* Chroma Representation                      -R
+* Probabilistic and graphical models
-* Obtaining MIR Data: MIDIfying your data - R
+** See http://soundlab.cs.princeton.edu/publications/2009_ismir_cba.pdf for Hoffman's example paper discussed in lecture
-* Other? -R
+* 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>Lab 3:</u> [http://ccrma.stanford.edu/workshops/mir2010/Lab3_2010.pdf Clustering lab with MFCCs]
+* If you finish early, see the "bonus labs" section below.
-<br><u>Day 4:</u> [http://ccrma.stanford.edu/workshops/mir2009/CCRMA_MIR_2009_Lecture_4.pdf Lecture 4 Slides]
+<br><u>Day 4:</u> [http://ccrma.stanford.edu/workshops/mir2010/CCRMA_MIR_2010_Lecture4.pdf Lecture 4 Slides - Part 1]
-* Boosting -R
+* Obtaining MIR Data: Social mining and MIR games, datasets
-* Structural Analysis  -R
+** [https://ccrma.stanford.edu/workshops/mir2010/DataResources.pdf Data resources list]
-** Similarity Matrix
+** Also see https://ccrma.stanford.edu/wiki/MIR_workshop_2010#Audio_Source_Material and https://ccrma.stanford.edu/wiki/MIR_workshop_2008_notes#Research_Databases_.2F_Collections_of_Ground_truth_data_and_copyright-cleared_music
-* Obtaining MIR Data: Social mining and MIR games - R
+* Classification: GMM
+* Classification examples:
-* Classification: GMM   -J
+** [http://ccrma.stanford.edu/workshops/mir2010/SpeakerRecognition.pdf Speaker Identification ]
-* Classification examples:  -J
-** Speech/Music Discrimination
 ** Genre Classification
+* 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]
 <br><u>Lab 4: </u>
-* [http://ccrma.stanford.edu/workshops/mir2009/Lab4/lab4.html Structural analysis]
+* [http://ccrma.stanford.edu/workshops/mir2010/Lab4_2010.pdf GMM Lab]
-* [http://ccrma.stanford.edu/workshops/mir2009/Lab%204%20-%20Gaussian%20Mixture%20Models.pdf GMM Lab]
+* [https://ccrma.stanford.edu/workshops/mir2010/Lab4Wekinator.pdf Wekinator Lab]
-<br><u>Day 5:</u> [http://ccrma.stanford.edu/workshops/mir2009/CCRMA_MIR_2009_Lecture_5.pdf Lecture 5 Slides]
+<br><u>Day 5:</u> [http://ccrma.stanford.edu/workshops/mir2010/CCRMA_MIR_2010_Lecture5.pdf Lecture 5 Slides - Part 1]
-* Building and evaluating systems - assembling testing and training sets - J
+* Building and evaluating systems - assembling testing and training sets
-* Classification: SVM - J
+* Classification: SVM
-* IR Evaluation Metrics (precision, recall, f-measure, AROC,...)   -R
+* 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]
-* Practical tips & tricks                -R/J
+* Practical tips & tricks
+** PCA & LDA
 * Student-Selected Optional Lecture Topics:
-** Options include:
+** Playlist generation
-*** Playlist generation
+** Visualization
-*** Social
+** Similarity and recommendation
-*** Visualization
+** Feature selection
-*** Fingerprinting
+** Fingerprinting
-**** See also: http://www.ee.columbia.edu/~dpwe/e4896/practicals.html#20100421
+*** See also: http://www.ee.columbia.edu/~dpwe/e4896/practicals.html#20100421
+** MIREX
-** Statistical Techniques and Feature Selection (PCA/LDA)
+** Structural analysis
-*** [http://ccrma.stanford.edu/workshops/mir2009/Lab5/lab5.tgz Data preprocessing using LDA]
+** [https://ccrma.stanford.edu/workshops/mir2009/Lab4/lab4.m Similarity Matrix]
-*** [http://ccrma.stanford.edu/workshops/mir2009/Lab5/stprtool.zip Download Statistical Pattern Recognition Toolbox]
-*** [http://ccrma.stanford.edu/workshops/mir2009/Lab5/stprtool.pdf Stprtool User's Guide]
-*** [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/8_classification.pdf More classification (lecture slides from Juan Bello)]                                              -J
 <br><u>Lab 5</u>
-* [http://ccrma.stanford.edu/workshops/mir2009/Lab%205%20-%20SVMs.pdf Building classifiers with SVMs]
+* [http://ccrma.stanford.edu/workshops/mir2010/Lab5_2010.pdf Building classifiers with SVMs]
+* [http://ccrma.stanford.edu/workshops/mir2010/miniMirex.pdf miniMIREX]
-<br><u>Removed by JayL on 6-30-10:</u>
+<br><u>Bonus Lab material</u>
-* Onset detection
+* [http://ccrma.stanford.edu/workshops/mir2010/weka_lab1.pdf Getting started with Weka]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab1/CATbox_v0.zip download CATbox (Computer Audition Toolbox): CATbox_v0.zip]
+* Harmony Analysis Slides / Labs
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab1/lab1_5.m Onset Time-domain method (lab1_5.m)]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab1/lab1_6.m Frequency-domain method: lab1_6.m]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab1/lab1_7.m Phase-based method: lab1_7.m]
-* Temporal Analysis:
-** Sub-Band Analysis
-** Post-Processing, Peak-Picking
-** Tempo estimation, beat tracking
-** [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/4_rhythm.pdf Temporal Analysis (lecture slides from Juan Bello)]
-* Novelty Score
-** Music Segmentation
-* [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/7_segmentation.pdf Structural Analysis (lecture slides from Juan Bello)]
-* [http://ccrma.stanford.edu/workshops/mir2009/references/Foote_00.pdf Automatic Audio Segmentation (Jonathan Foote)]
-* Key-Profile and Key Estimation             -K
-** Chord Recognition                          -K
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.tgz download lab3.tgz]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.html Key estimation, chord recognition]
 ** [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)]
-* New Features
+** [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.tgz Lab - download lab3.tgz]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab2/labrosa-coversongid.tgz download Dan Ellis' coversong id toolbox: coversongs]
+** [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.html Lab - Key estimation, chord recognition]
-** [http://ccrma.stanford.edu/workshops/mir2009/wav/04_rock_and_roll_music.wav download an audio file]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab2/post_proc.m post processing]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab2/adpthresholding.m adaptive thresholding]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab2/lab2_1.m Tempo estimation, beat tracking]
 == 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....]
-== MIR Workshop and lectures from 2008==
+== Past CCRMA MIR Workshops and lectures==
-[http://cm-wiki.stanford.edu/wiki/MIR_workshop_2008 CCRMA MIR Summer Workshop 2008]
+* [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 ==
@@ Line 241: / Line 172: @@
 == Audio Source Material ==
 OLPC Sound Sample Archive (8.5 GB) [http://wiki.laptop.org/go/Sound_samples]
+http://www.tsi.telecom-paristech.fr/aao/en/category/database/
 RWC Music Database (n DVDs) [available in Stanford Music library]
@@ Line 249: / Line 182: @@
 [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 ==