Difference between revisions of "MIR workshop 2010"
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= [https://ccrma.stanford.edu/wiki/MIR_workshop_2011 FOR THE LATEST MIR WORKSHOP - July 2011 - PLEASE VISIT HERE ] =
== Abstract ==
== Abstract ==
Revision as of 20:56, 29 July 2011
Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval
- 1 Logistics
- 2 FOR THE LATEST MIR WORKSHOP - July 2011 - PLEASE VISIT HERE
Workshop Title: "Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval"
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
- Netlab Pattern Recognition and Clustering Toolbox (Matlab)
- libsvm SVM toolbox (Matlab)
- MIR Toolboxes (Matlab)
- UCSD CatBox
- MA Toolbox
- MIDI Toolbox
- [see also below references]
- 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
- 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)
- Decision boundaries, decision stumps, and decision trees
- Classifier evaluation (Cross-validation, training and test sets)
Lab 2: [Lab 2
- If you finish early, see the "bonus labs" section below.
- 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
- Classification: GMM
- Classification examples:
- Speaker Identification
- Genre Classification
- Overview of Weka & the Wekinator
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,...)
- Practical tips & tricks
- PCA & LDA
- Student-Selected Optional Lecture Topics:
- Playlist generation
- Similarity and recommendation
- Feature selection
- Structural analysis
- Similarity Matrix
Bonus Lab material
- Getting started with Weka
- Harmony Analysis Slides / Labs
Supplemental papers and information for the lectures...
Past CCRMA MIR Workshops and lectures
References for additional info
- 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:
- The Mathworks' Matlab Tutorial
- ISMIR2007 MIR Toolbox Tutorial
- 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.
Audio Source Material
OLPC Sound Sample Archive (8.5 GB) 
RWC Music Database (n DVDs) [available in Stanford Music library]
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