Difference between revisions of "MIR workshop 2015"

Latest revision as of 13:33, 17 July 2015

Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval

News

Wednesday, July 15

8:48 am:

Today: Zafar Rafii, Jeff Scott, Aneesh Vartakavi, et al. of Gracenote will join us for lunch and for guest lectures in the afternoon.
If you checked out https://github.com/stevetjoa/stanford-mir onto your local machine, be sure to git checkout gh-pages before working.

Tuesday, July 14

9:31 am:

Don't forget %matplotlib inline at the top of your notebooks.

Monday, July 13

2:18 pm: dependencies:

apt-get install: git, python-dev, pip, python-scipy, python-matplotlib
Python packages: pip, boto, boto3, matplotlib, ipython, numpy, scipy, scikit-learn, librosa, mir_eval, seaborn, requests
(Anaconda)

11:11 am: Your post-it notes:

content-based analysis e.g. classifying violin playing style (vibrato, bowing)
MIR overview; music recommendation
feature extraction; dimensionality reduction; prediction
source separation techniques
chord estimation; "split" musical instruments; find beats in a song
audio-to-midi; signal/source/speaker separation; programming audio in Python (in general)
acoustic fingerprinting
machine learning; turn analysis -> synth; music characterization
beat tracking; ways of identifying timbre
mood recognition
instrument separation; real-time processing
Marsyas?
speed of retrieval
what's possible and what's not in music information retrieval; how to use MIR toolbox for fast realization of ideas
machine learning techniques for more general audio problems i.e. language detection or identifying sound sources
networking and getting to know you all

Attendees

Eric Raymond <lowlifi@gmail.com>, Stelios Andrew Stavroulakis, Richard Mendelsohn, Naithan Bosse, Alessio Bazzica, Karthik Yadati, Martha Larson, Stephen Hartzog, Philip Lee, Jaeyoung Choi, Matthew Gallagher, Yule Wu, Mark Renker, Rohit Ainapure, Eric Tarr <erictarr@gmail.com>, Allen Wu, Aaron Hipple

Logistics

Monday, July 13, through Friday, July 17, 2015. 9 AM to 5 PM every day.
Location: The Knoll, CCRMA, Stanford University. http://goo.gl/maps/nNKx
Instructors:
- Steve Tjoa
- Jay LeBoeuf, Real Industry.,

Abstract

How would you "Google for audio", provide music recommendations based on your MP3 files, or have a computer "listen" and understand what you are playing?

This workshop will teach such 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 to, understand, and 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 sort, search, recommend, tag, and transcribe music, possibly in real time.

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 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 Python is desired but not required. 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 audio analysis systems.

Schedule

Instructional material can be found at musicinformationretrieval.com (read only) or on GitHub (full source).

Day 1: Introduction to MIR, Signal Analysis, and Feature Extraction

Lecture

Introductions

CCRMA Introduction - (Nette, Fernando).
Introduction to MIR (What is MIR? Why MIR? Commercial applications)
Basic MIR system architecture
Timing and Segmentation: Frames, Onsets
Classification: Instance-based classifiers (k-NN)

Overview: Signal Analysis and Feature Extraction for MIR Applications

Windowed Feature Extraction
Feature-vector design (Overview: http://www.create.ucsb.edu/~stp/PostScript/PopeHolmKouznetsov_icmc2.pdf)
Time-domain features
Frequency-domain features

MFCCs sonified

Original track ("Chewing Gum") [1]
MFCCs only [2]

Lab

Understanding Audio Features Through Sonification

Background for students needing a refresher: Fundamentals of Digital Audio Signal Processing (lecture slides from Juan Bello)
Reminder: Save all your work, because you may want to build on it in subsequent labs.

Day 2: Pitch and Chroma Analysis; Machine Learning, Clustering and Classification

Lecture

Classification: Unsupervised vs. Supervised, k-means, GMM, SVM

Pitch and Chroma

Features:
- Monophonic Pitch Detection
- Polyphonic Pitch Detection
- Pitch representations (Tuning Histograms, Pitch and Pitch Class Profiles, Chroma)
Analysis:
- Dynamic Time Warping
- Hidden Markov Models
- Harmonic Analysis/Chord and Key Detection
Applications
- Audio-Score Alignment
- Cover Song Detection
- Query-by-humming
- Music Transcription

Lab

K-NN Instrument Classification

MFCC, K-Means Clustering

K-Means (2012)

Bonus Slides: Temporal & Harmony Analysis

Day 3: Deep Belief Networks; Pitch Transcription

Introduction to Deep Learning Slides

[ https://ccrma.stanford.edu/workshops/mir2014/fann_en.pdf Neural Networks made easy]

Pitch Transcription Exercise

Guest lectures by Gracenote

Catch-up from yesterday

Day 4: Music Information Retrieval in Polyphonic Mixtures

Lecture

Music Transcription and Source Separation

Nonnegative Matrix Factorization
Sparse Coding

Evaluation Metrics for Information Retrieval

Lab

Lab 4 Description

References:

IR Evaluation Metrics (precision, recall, f-measure, AROC,...)
- Recall-Precision
- ROC Analysis

Day 5: Hashing for Music Search and Retrieval

Locality Sensitive Hashing (notebook)

Lunch at The Oasis

Software Libraries

Supplemental papers and information for the lectures...

Past CCRMA MIR Workshops and lectures

Additional References

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

Background material:

Papers:

ISMIR 2011 Proceedings: http://ismir2011.ismir.net/program.html
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:

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) [3]

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

https://ccrma.stanford.edu/wiki/MIR_workshop_2008_notes#Research_Databases_.2F_Collections_of_Ground_truth_data_and_copyright-cleared_music

Difference between revisions of "MIR workshop 2015"

Latest revision as of 13:33, 17 July 2015

Contents

News

Attendees

Logistics

Abstract

Schedule

Day 1: Introduction to MIR, Signal Analysis, and Feature Extraction

Day 2: Pitch and Chroma Analysis; Machine Learning, Clustering and Classification

Day 3: Deep Belief Networks; Pitch Transcription

Day 4: Music Information Retrieval in Polyphonic Mixtures

Day 5: Hashing for Music Search and Retrieval

Software Libraries

Supplemental papers and information for the lectures...

Past CCRMA MIR Workshops and lectures

Additional References

Audio Source Material

Navigation menu

Views

Personal tools

Navigation

Search

Tools

@@ Line 1: / Line 1: @@
 ''' Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval '''
+== News ==
+'''Wednesday, July 15'''
+:48 am:
+* Today: Zafar Rafii, Jeff Scott, Aneesh Vartakavi, et al. of Gracenote will join us for lunch and for guest lectures in the afternoon.
+* If you checked out https://github.com/stevetjoa/stanford-mir onto your local machine, be sure to '''git checkout gh-pages''' before working.
+'''Tuesday, July 14'''
+:31 am:
+* Don't forget '''%matplotlib inline''' at the top of your notebooks.
+'''Monday, July 13'''
+:18 pm: dependencies:
+* apt-get install: git, python-dev, pip, python-scipy, python-matplotlib
+* Python packages: pip, boto, boto3, matplotlib, ipython, numpy, scipy, scikit-learn, librosa, mir_eval, seaborn, requests
+* (Anaconda)
+:11 am: Your post-it notes:
+* content-based analysis e.g. classifying violin playing style (vibrato, bowing)
+* MIR overview; music recommendation
+* feature extraction; dimensionality reduction; prediction
+* source separation techniques
+* chord estimation; "split" musical instruments; find beats in a song
+* audio-to-midi; signal/source/speaker separation; programming audio in Python (in general)
+* acoustic fingerprinting
+* machine learning; turn analysis -> synth; music characterization
+* beat tracking; ways of identifying timbre
+* mood recognition
+* instrument separation; real-time processing
+* Marsyas?
+* speed of retrieval
+* what's possible and what's not in music information retrieval; how to use MIR toolbox for fast realization of ideas
+* machine learning techniques for more general audio problems i.e. language detection or identifying sound sources
+* networking and getting to know you all
+== Attendees ==
+Eric Raymond <lowlifi@gmail.com>, Stelios Andrew Stavroulakis, Richard Mendelsohn, Naithan Bosse, Alessio Bazzica, Karthik Yadati, Martha Larson, Stephen Hartzog, Philip Lee, Jaeyoung Choi, Matthew Gallagher, Yule Wu, Mark Renker, Rohit Ainapure, Eric Tarr <erictarr@gmail.com>, Allen Wu, Aaron Hipple
 == Logistics ==
@@ Line 21: / Line 67: @@
 '''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 audio analysis systems.
-== Schedule: Lectures & Labs ==
+== Schedule  ==
-=== Day 1: Introduction to MIR, Signal Analysis and Feature Extraction ===
+Instructional material can be found at [http://musicinformationretrieval.com musicinformationretrieval.com] (read only) or on [https://github.com/stevetjoa/stanford-mir GitHub] (full source).
-<br><u>Day 1: Part 1</u> [http://ccrma.stanford.edu/workshops/mir2014/CCRMA_MIR2014_Lecture1.pdf Lecture 1 Slides]
+=== Day 1: Introduction to MIR, Signal Analysis, and Feature Extraction ===
-* Introductions
+'''Lecture'''
-* CCRMA Introduction - (Nette, Carr, Fernando).
-* Introduction to MIR (What is MIR? Why are people interested? Commercial Applications of MIR)
+Introductions
-* Overview of a basic MIR system architecture
+* CCRMA Introduction - (Nette, Fernando).
+* Introduction to MIR (What is MIR? Why MIR? Commercial applications)
+* Basic MIR system architecture
 * Timing and Segmentation: Frames, Onsets
-* Demo: Using simple heuristics and thresholds (i.e. "Why do we need machine learning?")
 * Classification: Instance-based classifiers (k-NN)
-* Information Retrieval Basics (Part 1)
-** Classifier evaluation (Cross-validation, training and test sets)
-<br><u>Day 1: Part 2</u> [http://ccrma.stanford.edu/workshops/mir2014/CCRMA_MIR2014_Lecture2.pdf Lecture 2 Slides]
+Overview: Signal Analysis and Feature Extraction for MIR Applications
-* Overview: Signal Analysis and Feature Extraction for MIR Applications
 * Windowed Feature Extraction
-** I/O and analysis loops
 * Feature-vector design (Overview: http://www.create.ucsb.edu/~stp/PostScript/PopeHolmKouznetsov_icmc2.pdf)
-** Kinds/Domains of Features
+* Time-domain features
-** Application Requirements (labeling, segmentation, etc.)
-* Time-domain features (MPEG-7 Audio book ref)
-** RMS, Peak, LP/HP RMS, Dynamic range, ZCR
 * Frequency-domain features
-** Spectrum, Spectral bins
-** Spectral measures (Spectral statistical moments)
-** Pitch-estimation and tracking
-** MFCCs
-* Spatial-domain features
-** M/S Encoding, Surround-sound Processing Frequency-dependent spatial separation, LCR sources
-MFCCs Sonified<br>
-Original track ("Chewing Gum"): [https://myspace.com/anniemusic/music/song/chewing-gum-28101163-14694] <br>
-MFCCs only [http://www.cs.princeton.edu/~mdhoffma/icmc2008/] <br>
+MFCCs sonified
+* Original track ("Chewing Gum") [https://myspace.com/anniemusic/music/song/chewing-gum-28101163-14694]
+* MFCCs only [http://www.cs.princeton.edu/~mdhoffma/icmc2008/]
-<br><u>Lab 1:</u> <br>
-* Application: Instrument recognition and drum transcription / Using simple heuristics and thresholds (i.e. "Why do we need machine learning?")
+'''Lab'''
-* [http://nbviewer.ipython.org/github/stevetjoa/stanford-mir/blob/master/Table_of_Contents.ipynb Lab 1 - Basic Feature Extraction and Classification] <br>
+[http://musicinformationretrieval.com/feature_sonification.html Understanding Audio Features Through Sonification]
-* From your home directory, simply type the following to obtain a copy of the repository: <code>git clone https://github.com/stevetjoa/ccrma.git</code>
+* 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)]
-** To receive an up-to-date version of the repository, from your repository folder: <code>git pull</code>
+* ''Reminder'': Save all your work, because you may want to build on it in subsequent labs.
-* Background for students needing a refresher:
+=== Day 2: Pitch and Chroma Analysis; Machine Learning, Clustering and Classification ===
-** [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/2_fft.pdf Fundamentals of Digital Audio Signal Processing (lecture slides from Juan Bello)]
-* REMINDER: Save all your work, because you may want to build on it in subsequent labs.
+'''Lecture'''
-=== Day 2: Beat, Rhythm, Pitch and Chroma Analysis ===
+Classification: Unsupervised vs. Supervised, k-means, GMM, SVM
-Presenters: Leigh Smith, Steve Tjoa
-<br><u>Day 2: Part 1 Beat-finding and Rhythm Analysis</u> [http://ccrma.stanford.edu/workshops/mir2014/CCRMA_MIR2014_Lecture3.pdf Lecture 3 Slides]
+Pitch and Chroma
-[http://ccrma.stanford.edu/workshops/mir2011/BeatReferences.pdf A list of beat tracking references cited]
-Demo: MediaMined Discover ([https://discover.izotope.com/ Rhythmic Similarity])
-* Onset-detection: Many Techniques
-** Time-domain differences
-** Spectral-domain differences
-** Perceptual data-warping
-** Adaptive onset detection
-* Beat-finding and Tempo Derivation
-** IOIs and Beat Regularity, Rubato
-*** Tatum, Tactus and Meter levels
-*** Tempo estimation
-** Onset-detection vs Beat-detection
-*** The Onset Detection Function
-** Approaches to beat tracking & Meter estimation
-*** Autocorrelation
-*** Beat Spectrum measures
-*** Multi-resolution (Wavelet)
-** Beat Histograms
-** Fluctuation Patterns
-** Joint estimation of downbeat and chord change
-<br><u>Day 2, Part 2: Pitch and Chroma Analysis</u> [http://ccrma.stanford.edu/workshops/mir2013/CCRMA_MIR2013_pitch.pdf Lecture 4 Slides]
 * Features:
 ** Monophonic Pitch Detection
@@ Line 114: / Line 120: @@
 ** Query-by-humming
 ** Music Transcription
+'''Lab'''
-CCRMA Tour
+[http://musicinformationretrieval.com/knn_instrument_classification.html K-NN Instrument Classification]
-'''Lab 2:'''
+[http://musicinformationretrieval.com/kmeans_instrument_classification.html MFCC, K-Means Clustering]
-Part 1: Tempo Extraction
-Part 2: Add in MFCCs to classification and test w Cross validation
-* [https://github.com/stevetjoa/ccrma#lab-2 Lab 2 description]
-*  See [https://github.com/stevetjoa/ccrma/blob/master/odf_of_file.m Onset Detection Function example] within the MIR matlab codebase in Octave/Matlab.
-* Bonus Slides: Temporal & Harmony Analysis
+* [http://ccrma.stanford.edu/workshops/mir2012/2012-ClusterLab.pdf K-Means (2012)]
-** [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/4_rhythm.pdf Temporal Analysis (lecture slides from Juan Bello)]
-** [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)]
-=== Day 3: Machine Learning, Clustering and Classification ===
+Bonus Slides: Temporal & Harmony Analysis
-Demo: iZotope Discover (Sound Similarity Search, jay) [http://www.izotope.com/tech/cloud/mediamined.asp  Video]
+* [http://ccrma.stanford.edu/workshops/mir2009/juans_lecture/4_rhythm.pdf Temporal Analysis (lecture slides from Juan Bello)]
+* [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)]
-Guest Lecture: Stephen Pope (SndsLike, BirdGenie)
-[https://ccrma.stanford.edu/workshops/mir2014/MAT_MIR4-update.pdf MAT_MIR4-update slides]
-[https://ccrma.stanford.edu/workshops/mir2014/BirdsEar.pdf BirdGenie Slides]
-[https://ccrma.stanford.edu/workshops/mir2014/SndsLike.pdf SndsLike Slides]
-Lecture 5: Classification: Unsupervised vs. Supervised, k-means, GMM, SVM - Steve [http://ccrma.stanford.edu/workshops/mir2014/CCRMA_MIR2014_ML.pdf Lecture 5 Slides]
+=== Day 3: Deep Belief Networks; Pitch Transcription ===
+Introduction to Deep Learning [https://ccrma.stanford.edu/workshops/mir2013/CCRMA_MIR2013_DBN.pdf Slides]
-'''Lab 3'''
+[ https://ccrma.stanford.edu/workshops/mir2014/fann_en.pdf Neural Networks made easy]
-Topic: MFCC + k-Means, Clustering
-* [http://ccrma.stanford.edu/workshops/mir2012/2012-ClusterLab.pdf K-Means]
+Pitch Transcription Exercise
+Guest lectures by Gracenote
+Catch-up from yesterday
-Matlab code for key estimation, chord recognition:
-* [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.tgz Lab - download lab3.tgz]
-* [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.html Lab - Key estimation, chord recognition]
 === Day 4: Music Information Retrieval in Polyphonic Mixtures ===
-Lecture 6: Steve Tjoa, [http://ccrma.stanford.edu/workshops/mir2013/ccrma20130627.pdf Lecture 6 Slides]
+'''Lecture'''
-* Music Transcription and Source Separation
+Music Transcription and Source Separation
 * Nonnegative Matrix Factorization
 * Sparse Coding
-Guest Lecture 7: Andreas Ehmann, MIREX <br>
+Evaluation Metrics for Information Retrieval
-Lecture 8: Evaluation Metrics for Information Retrieval - Leigh Smith [https://ccrma.stanford.edu/workshops/mir2014/CCRMA_MIR2014_IR.pdf Slides]
+'''Lab'''
-'''Lab 4'''
+[https://github.com/stevetjoa/ccrma#lab-4 Lab 4 Description]
-* [https://github.com/stevetjoa/ccrma#lab-4 Lab 4 Description]
 References:
@@ Line 169: / Line 167: @@
 ** [http://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf ROC Analysis]
-=== Day 5: Deep Belief Networks and Wavelets ===
-Lecture 10: Steve Tjoa, Introduction to Deep Learning [https://ccrma.stanford.edu/workshops/mir2013/CCRMA_MIR2013_DBN.pdf Slides]
+=== Day 5: Hashing for Music Search and Retrieval  ===
-Lecture 11: Leigh Smith, An Introduction to Wavelets [https://ccrma.stanford.edu/workshops/mir2014/CCRMA_MIR2014_Wavelets.pdf Slides]
+Locality Sensitive Hashing ([http://musicinformationretrieval.com/lsh_fingerprinting.html notebook])
-[ https://ccrma.stanford.edu/workshops/mir2014/fann_en.pdf Neural Networks made easy]
 Lunch at [http://en.wikipedia.org/wiki/Homebrew_Computer_Club The Oasis]
-Klapuri eBook:  http://link.springer.com/book/10.1007%2F0-387-32845-9
+== Software Libraries ==
-Afternoon: CCRMA Lawn BBQ
-== software, libraries, examples ==
-Applications & Environments
-* [http://www.mathworks.com/products/matlab/ MATLAB]
-* [http://www.cs.waikato.ac.nz/ml/weka/ Weka Machine Learning and Data Mining Toolbox (Standalone app / Java)]
-Machine Learning Libraries & Toolboxes
+* [https://www.python.org/ Python]
-* [http://www.ncrg.aston.ac.uk/netlab/ Netlab Pattern Recognition and Clustering Toolbox (Matlab)]
+* [http://www.numpy.org/ NumPy]
-* [http://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab libsvm SVM toolbox (Matlab)]
+* [http://www.scipy.org/ SciPy]
-* [http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox/Download/fg_base_view MIR Toolboxes (Matlab)]
+* [http://ipython.org/ IPython]
-* [http://cosmal.ucsd.edu/cal/projects/CATbox/catbox.htm UCSD CatBox]
+* [http://scikit-learn.org/stable/ scikit-learn]
-Optional Toolboxes
+* [http://bmcfee.github.io/librosa/ librosa]
-* [http://www.ofai.at/~elias.pampalk/ma/ MA Toolbox]
+* [http://craffel.github.io/mir_eval/ mir_eval]
-* [http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/miditoolbox MIDI Toolbox]
+* [http://essentia.upf.edu/ Essentia]
-* [see also below references]
+* [http://www.vamp-plugins.org/vampy.html VamPy]
-* [http://marsyas.sness.net/ 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/
 == Supplemental papers and information for the lectures...==
@@ Line 216: / Line 199: @@
 * [http://cm-wiki.stanford.edu/wiki/MIR_workshop_2008 CCRMA MIR Summer Workshop 2008]
-== References for additional info ==
+== Additional References ==
 Recommended books:
 * Data Mining: Practical Machine Learning Tools and Techniques, Second Edition by Ian H. Witten , Eibe Frank (includes software)
@@ Line 224: / Line 208: @@
 * Speech and Audio Signal Processing:Processing and perception of speech and music Ben Gold & Nelson Morgan, Wiley 2000
-Prerequisite / background material:
+Background material:
 * http://140.114.76.148/jang/books/audioSignalProcessing/
-* [http://ccrma.stanford.edu/workshops/mir2008/learnmatlab_sp3.pdf The Mathworks' Matlab Tutorial]
 * [http://ismir2007.ismir.net/proceedings/ISMIR2007_tutorial_Lartillot.pdf ISMIR2007 MIR Toolbox Tutorial]
@@ Line 245: / Line 228: @@
 * http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials
 * http://www.music-ir.org/evaluation/tools.html
-* http://140.114.76.148/jang/matlab/toolbox/
 * http://htk.eng.cam.ac.uk/
@@ Line 262: / Line 244: @@
 https://ccrma.stanford.edu/wiki/MIR_workshop_2008_notes#Research_Databases_.2F_Collections_of_Ground_truth_data_and_copyright-cleared_music
-== MATLAB Utility Scripts ==
-* [http://ccrma.stanford.edu/~mw/ Mike's scripts]
-* [[Reading MP3 Files]]
-* [[Low-Pass Filter]]
-* Steve Tjoa: [http://ccrma.stanford.edu/~kiemyang/software Matlab code] (updated July 9, 2009)
-[[Category: Workshops]]
-http://ccrma.stanford.edu/~kglee/kaist_summer2008_special_lecture/
-[[MIR_workshop_2014]]
-=== Bonus Lab Material from Previous Years (Matlab) ===
-* Harmony Analysis Slides / Labs
-** [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)]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.tgz Lab - download lab3.tgz]
-** [http://ccrma.stanford.edu/workshops/mir2009/Lab3/lab3.html Lab - Key estimation, chord recognition]
-** [http://ccrma.stanford.edu/workshops/mir2013/Lab5-SVMs.htm  SVM Lab]
-* 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]
-** [http://ccrma.stanford.edu/workshops/mir2011/weka_lab1.pdf Getting started with Weka]
-** [https://ccrma.stanford.edu/workshops/mir2011/Wekinator_lab_2011.pdf Wekinator Lab]
-* Downloads
-**  [https://ccrma.stanford.edu/workshops/mir2011/MAT240F-Reader.zip UCSB MAT 240F Reader]
-**  [https://ccrma.stanford.edu/workshops/mir2011/MAT240F-Code.zip UCSB MAT 240F Code]
-**  [https://ccrma.stanford.edu/workshops/mir2011/MAT240F-Sounds.zip UCSB MAT 240F Sounds]
-* A brief history of MIR
-** See also http://www.ismir.net/texts/Byrd02.html
-* Notes
-** CAL500 decoding
-for i in *.mp3; do echo $i; afconvert -d BEI16@44100 -f AIFF "$i"; done
-* Extract CAL 500 per-song features to .mat or .csv using features from today.  This will be used on lab for Friday. Copy it from the folder ccrma-gate.stanford.edu:/usr/ccrma/workshops/mir2011/cal500.tar (beware it's a 2Gb .tar file!) or grab the AIFF versions from ccrma-gate.stanford.edu:/usr/ccrma/workshops/mir2011/cal500_aiffs.tar (that's 16 GB)