Revision as of 07:30, 29 June 2009

Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval

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

9-5 PM. June 29-July 3 2009
Instructors: Jay LeBoeuf and Kyogu Lee

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.

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

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 2009

Day 1

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:
- Fundamentals of Matlab
- Fundamentals of Digital Audio Signal Processing (FFT, STFT, Windowing, Zero-padding, 2-D Time-frequency representation)
STFT and time-frequency representation of audio [1]

Audio slice manipulation with a simple feature.

Lab 1 -"Playing with audio slices"

Day 2

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

Lab 2

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

Lab 3

Day 4

Structural Analysis 1 -K
- Similarity Matrix
- Novelty Score
- Music Segmentation

New Classifier: GMM -J
Classification examples: -J
- Genre Classification
- Instrument Identification
- Speech/Music Discrimination

Lab 4

Day 5

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

Lab 5

Supplemental papers and information for the lectures...

Explanations, tutorials, code demos, recommended papers here - for each topic....

Jay's Lectures 2008

CCRMA MIR 2008 Workshop Wiki - complementary study notes for these lectures.

Lecture 1 - Introduction and motivations for MIR. Onset detection, zero crossing rate, and heuristic classification.

Lecture 2 - k-NN, spectral moments, spectral features, octave bands, profiles, and scaling.

Lecture 3 - Log spectrogram, chromagrams, key and chord estimation, discuss final projects.

Lecture 4 - Sonifying feature data, temporal feature extractors, MFCCs, unsupervised classification, k-means clustering.

Lecture 5 - Support Vector Machines: Binary, RBF parameters, grid search, practical information for designing SVM classifiers.

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.

Lecture 8 - Practical considerations for building classifiers, Hidden Markov Models.

Lecture 9 - Code sharing resources, MPEG-7 feature extractors.

Lecture 10 - Course summary, acknowledgments, resources for research.

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) [2]

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

MIRToolbox comments, wishes, wants

MATLAB Utility Scripts

Mike's scripts

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

@@ Line 113: / Line 113: @@
 * Background for students needing a refresher:
 ** [http://ccrma.stanford.edu/~kglee/kaist_summer2008_special_lecture/lab/lab0/lab0.html Fundamentals of Matlab ]
-** [http://ccrma.stanford.edu/~kglee/kaist_summer2008_special_lecture/lab/lab1/lab1.html Fundamentals of Digital Audio Signal Processing (FFT, STFT, Windowing, Zero-padding, 2-D Time-frequency representation)]
+** [http://ccrma.stanford.edu/workshops/mir2009/Lab/lab1/lab1.html Fundamentals of Digital Audio Signal Processing (FFT, STFT, Windowing, Zero-padding, 2-D Time-frequency representation)]
-* STFT and time-frequency representation of audio [http://ccrma.stanford.edu/~kglee/kaist_summer2008_special_lecture/lab/lab1/lab1_4.m Lab1_4.m]
+* STFT and time-frequency representation of audio [http://ccrma.stanford.edu/workshops/mir2009/Lab/lab1/lab1.html]
 Audio slice manipulation with a simple feature.
 * [http://ccrma.stanford.edu/workshops/mir2009/Lab%201%20-%20Playing%20with%20audio%20slices.pdf Lab 1 -"Playing with audio slices"]

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