Difference between revisions of "MIR workshop 2010"

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== Lectures & Labs 2009 ==
 
== Lectures & Labs 2009 ==
 
<br><u>Day 1</u>
 
<br><u>Day 1</u>
−
* CCRMA Introduction (Carr/Sasha)  -J/F
+
* CCRMA Introduction (Carr/Sasha)  -J/R
 
* Introduction to MIR (What is MIR? Why are people interested?) -J  
 
* Introduction to MIR (What is MIR? Why are people interested?) -J  
 
* Overview of a basic MIR system architecture    -J
 
* Overview of a basic MIR system architecture    -J
Line 120: Line 120:
 
* Scaling of feature data    -J
 
* Scaling of feature data    -J
 
* Classification: Instance-based classifiers (such as k-NN and distance metrics)  -J
 
* Classification: Instance-based classifiers (such as k-NN and distance metrics)  -J
 +
* Obtaining MIR Data: MIR Data sets - R
 +
* Obtaining MIR Data: MIDIfying your data - R
 +
* Obtaining MIR Data: Social mining and MIR games - R
  
 
<br><u>Lab 2</u>
 
<br><u>Lab 2</u>

Revision as of 17:07, 30 June 2010

Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval

Logistics

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

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.

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

Lectures & Labs 2009


Day 1


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.

Audio slice manipulation with a simple feature.


Day 2

  • Features: Additional spectral features (Spread, Flatness) -J
  • Scaling of feature data -J
  • Classification: Instance-based classifiers (such as k-NN and distance metrics) -J
  • Obtaining MIR Data: MIR Data sets - R
  • Obtaining MIR Data: MIDIfying your data - R
  • Obtaining MIR Data: Social mining and MIR games - R


Lab 2


Day 3 - Harmony: Key, Chord Estimation

  • Features: Temporal features; Octave-bands -J
  • Features: Spectral Envelopes, MFCCs -J
  • Classification: Unsupervised classification (k-means) -J
  • Chroma Representation -K


Lab 3


Day 4

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


Lab 4


Day 5

  • Building and evaluating systems - assembling testing and training sets - J
  • IR Methodologies (Cross-validation, training and test sets) - R/J
  • Classification: SVM
  • Jay's Lecture 5 Slides
  • IR Evaluation Metrics (precision, recall, f-measure, AROC,...) -R
  • Practical tips & tricks -R/J



Lab 5



Removed by JayL on 6-30-10:

Supplemental papers and information for the lectures...

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

MIR Workshop and lectures from 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:

Papers:

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]

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

MATLAB Utility Scripts

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

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