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Information & Software Engineering Group

Music Information Retrieval

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Retrieval at TU Vienna IFS
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Audio Feature Extraction

SOM-Toolbox

Classification

 

How-To Collection

(Some topics are yet under construction. Ask for help via email.)
 

1 Audio Feature Extraction

The following points describe methods to extract various different features from audio files. In most cases only one of these methods is needed.

1.1 Matlab Audio Feature Extraction

  •  Rhythm Patterns, SSD, RH

1.2 M2K 

1.3 Marsyas 

The vector files with the extracted features can be used as input vectors for the SOM-Toolbox or for classification.

 

2 SOM-Toolbox

2.1 SOM-Toolbox How-To

2.2 SOM-Toolbox example Usage written by M. Dittenbach

2.3 SOMlib Vector format explained

 

 3 Classification

3.1 Weka - Machine Learning Java Tool

  • Download Weka. It is implemented in Java, thus platform independent. (On some Linux distributions there might appear problems with the window manager and/or Java 1.5, in that case download the file LookAndFeel.props and place it in your home directory.)
  • For Weka you need to convert SomLib vector files into the Weka .arff format. Download the conversion script from SOMLib to .arff format: (right click - Save target as)
    somlib2arff_wclass.pl
    (Perl must be installed to execute the script)
  • For classification (and thus for the above script) you need a groundtruth file, where each file to classify is labeled with a class.
    Create a groundtruth file as an Ascii file with a comma-separated list of files and their assigned class:
    <filename>,<class>
    If your files are stored in folders, whose names represent the class labels, (in Linux) you can use commands similar to these in order to create a groundtruth file:
    find . -name *.mp3 > filelist.txt
    cut -d / -f 2 filelist.txt > classes.txt
    paste filelist.txt classes.txt > filelist_wclasses.txt
  • Then use the script to create an .arff file:
    somlib2arff_wclass.pl <vector_file> filelist_wclasses.txt
  • Follow the Weka-Explorer Instructions for classification.

3.2 R (todo)

3.3 SVMlight (todo)

 
 
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last edited 23.03.2006 by Thomas Lidy

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