MIR workshop 2009
- 1 Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval
- 1.1 logistics
- 1.2 Abstract
- 1.3 Workshop syllabus
- 1.4 software, libraries, examples
- 1.5 Lectures & Labs 2009
- 1.6 Supplemental papers and information for the lectures...
- 1.7 Jay's Lectures 2008
- 1.8 References for additional info
- 1.9 Audio Source Material
- 1.10 MATLAB Utility Scripts
Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval
Workshop Title: "Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval"
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.
- 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
- 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
- Higher-level features
- Key Estimation
- Chord Estimation
- Genre (genre, artist ID, similarity)
Analysis / Decision Making
- Heuristic Analysis
- Clustering and probability density models
Model / Data Preparation Techniques
- Data Preparation
- Scaling data
- Model organization
- concept and design
- Data set construction and organization
- 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
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 2009
- CCRMA Introduction (Carr/Sasha) -J/K
- Introduction to MIR (What is MIR? Why are people interested?) -J
- Overview of a basic MIR system architecture -J
- Timing and Segmentation: Frames, Onsets -K
- Overview of frequency-based onset detection -K
- Features: ZCR, Spectral moments -K
- Classification: Using simple heuristics and thresholds -J
Lab 1 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.
- Onset detection
- Background for students needing a refresher:
- STFT and time-frequency representation of audio Lab1_4.m
Audio slice manipulation with a simple feature.
- Temporal Analysis: - K
- Sub-Band Analysis
- Post-Processing, Peak-Picking
- Tempo estimation, beat tracking
- Features: Additional spectral features (Spread, Flatness) -J
- Scaling of feature data -J
- Classification: Instance-based classifiers (such as k-NN and distance metrics) -J
- Tempo estimation, beat tracking
- Extract new features
- [Lab 2- Build simple classifiers using those features
Day 3 - Harmony: Key, Chord Estimation
- Features: Timbral features; Octave-bands -J
- Features: Spectral Envelopes, MFCCs) -J
- Classification: Unsupervised classification (k-means) -J
- Chroma Representation -K
- Key-Profile and Key Estimation -K
- Chord Recognition -K
- Structural Analysis 1 -K
- Similarity Matrix
- Novelty Score
- Music Segmentation
- New Classifier: GMM -J
- Classification examples: -J
- Genre Classification
- Instrument Identification
- Speech/Music Discrimination
- Building and evaluating systems - assembling testing and training sets - J
- IR Methodologies (Cross-validation, training and test sets) - K/J
- Classification: SVM -J
- IR Evaluation Metrics (precision, recall, f-measure, AROC,...) -K
- Practical tips & tricks -K/J
- Guest Lecture: Gautham Mysore
Supplemental papers and information for the lectures...
Jay's Lectures 2008
Lecture 6 - One-class SVM, nu parameter, accuracy, cross-validation, evaluation metrics, assembling training and testing data, probabilistic clustering with GMMs, GMM parameters, distance measures between PDFs, Expectation-Maximization, Artist and Genre classification.
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]
MATLAB Utility Scripts