Difference between revisions of "MIR workshop 2008 notes"

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(Model / Data Preparation Techniques)
(Model / Data Preparation Techniques)
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== Model / Data Preparation Techniques ==
 
== Model / Data Preparation Techniques ==
== Data Preparation
+
== Data Preparation ==
 
=== PCA / LDA ===  
 
=== PCA / LDA ===  
 
=== Scaling data ===  
 
=== Scaling data ===  
 
=== Model organization ===  
 
=== Model organization ===  
 
* concept, design, data set construction and organization
 
* concept, design, data set construction and organization

Revision as of 10:23, 1 August 2008

This page is intended to supplement the lecture material found in the class - providing extra tutorials, support, references for further reading, or demonstration code snippets for those interested in a given topic. Please contribute to this growing list of resources. Do you have a great explanation of how a technique works? Found a great Java applet that illustrates a concept? Discovered a great survey of the field for a particular area? Please add it for the benefit of future students. Thanks!

I encourage you to ADD links and sections - but please do not REMOVE headings or items from the page.

Timing and Segmentation

Onset Detection

Papers

Code

Beat Extraction

Papers

Code

Tempo Extraction

Papers

Code

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

MPEG-7

Higher-level features

Key Estimation

Chord Estimation

Genre (genre, artist ID, similarity)

"Fingerprints"

Analysis / Decision Making

Classification

Heuristic Analysis

Distance measures (Euclidean, Manhattan, etc.)

k-NN

SVM / One-class SVM

Clustering and probability density models

Density distance measures (centroid distance, EMD, KL-divergence, etc)

k-Means

Clustering

GMM

HMM

Nested classifier / Anchor-space / template-based systems

Model / Data Preparation Techniques

Data Preparation

PCA / LDA

Scaling data

Model organization

  • concept, design, data set construction and organization