Audacity is open source tool, for Windows/Mac/Linux platform. Using this tool you can easily edit audio tracks in multiple formats (Mp3, Flac, Wav, ...). I use it to edit audio tracks (cutting, pasting, etc. ) and for spectrum preview, but it can apply many effects to audio file like compressor, echo, low/high pass filtering. I has also implemented some MIR algorithms like onset detection or beat tracking.
You can download Audacity from this place.
Monday, 2 June 2014
Saturday, 31 May 2014
The MagnaTagATune Dataset
MagnaTagATune is a data set with almost 26 000 of 29 seconds audio tracks. Tracks are sampled with sampling rate 16 kHz, so bandwidth is limited to 8 kHz. Every sample is annotated in 189 categories with binary value. Dataset has annotation in CSV and MySQL, and some Python scripts available (I didn`t use them, I wrote my own available here ).
And here is some simple example of using my scripts:
MagnaTagATune is a nice dataset because of quite good annotation and large number of tracks. It can be used not only for genre classification but also mood classificationinstruments classification/detection.
You can download whole dataset from this place
And here is some simple example of using my scripts:
# Path of annotations, in this case folders with audio tracks
# should be placed in /home/user/Magnatagtune/
filename = '/home/user/Magnatagtune/annotations_final.csv'
# Open file with annotation
file = open(filename, 'rb')
# Creating CSVParser object
mangaCSV = CSVParser(file)
# Getting names of songs in singer category. Return list of string
print 'Files with singer category'
print mangaCSV.printFilesInCategory('singer')
# Getting all tags of track
print 'Categories of burnshee_thornside-rock_this_moon-01-bad_bad_luck-117-146'
print mangaCSV.printCategoriesOfFile('burnshee_thornside-rock_this_moon-01-bad_bad_luck-117-146')
MagnaTagATune is a nice dataset because of quite good annotation and large number of tracks. It can be used not only for genre classification but also mood classificationinstruments classification/detection.
You can download whole dataset from this place
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