annotate README.TXT @ 0:e9a9cd732c1e
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first hg version after svn
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wolffd |
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Tue, 10 Feb 2015 15:05:51 +0000 |
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1 This page allows you to reproduce the experiments from our paper "Feature Preprocessing using RBM for Music Similarity Learning". This includes the newest release of the CAMIR system for modelling music similarity.
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3 MLR and SVMLIGHT were tested using a framework developed by Daniel Wolff (daniel.wolff.1@city.ac.uk) at the MIRG group.
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4 This version of CAMIR contains the RBM toolbox of Son Tran (son.tran.1@city.ac.uk).
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5 You can download the code using subversion from the following repository. It is licensed under the GNU GPL v3.
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6
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7 user: anonymous
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8 pass: citymirg
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9 http://chivm.soi.city.ac.uk/svn/camir/branches/code_publications/AES2013/
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11 For reproducing the results in the paper using Matlab on a Windows machine follow these steps:
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12
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13 -Download the code from above using a subversion client such as collabnet svn and the above password
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14 -Set the working directories in the editme_startup.m file of the downloaded code to the location of the downloaded code
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15 -Include the code directory with all subdirectories in your matlab path
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16 -Start Matlab, change the working directory to the location of the downloaded code
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17 -Edit and execute the editme_startup file, this initialises the dataset and could take a few minutes.
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18 -Run the experiments using the scripts in the folder reproduce_AES53rd.
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19 -Most of the results are output as figures. Otherwise values are printed in the Matlab console
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20
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21 If you use this code please cite our paper. We are more than welcoming your additional bits of functionality to be included in the package. |
