# HG changeset patch
# User Gerard Roma <gerard.roma@upf.edu>
# Date 1383561965 0
# Node ID def2b3fa14503f4e074977e9d93ce27b0b639c0c
# Parent  96b1b8697b607d8f8f90a4318252ba9bfc437a9f
corrected README

diff -r 96b1b8697b60 -r def2b3fa1450 README.md
--- a/README.md	Mon Nov 04 10:43:51 2013 +0000
+++ b/README.md	Mon Nov 04 10:46:05 2013 +0000
@@ -1,9 +1,11 @@
-The files in this folder represent our submission for the Scene Classificatoin task of the IEEE D-CASE AASP Challenge (http://c4dm.eecs.qmul.ac.uk/sceneseventschallenge/)
+The files in this repository represent our submission for the Scene Classificatoin task of the IEEE D-CASE AASP Challenge (http://c4dm.eecs.qmul.ac.uk/sceneseventschallenge/)
 
-The code has been tested mainly on Matlab2012 on OSX
+The code has been tested mainly on Matlab2012 on OSX.
+
 Required libraries: rastamat and libsvm
 
 The implemented approach is described in:
+
 G. Roma, W. Nogueira, P.Herrera, _Recurrence Quantification Analysis Features for Environmental Sound Recognition_. Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. New Paltz, USA 2013.
 
 The main files are:
@@ -12,12 +14,12 @@
 * analyze_files.m
 * RQA.m
 
-The rest of matlab files can be used to test the code. Some of the files are taken from: https://soundsoftware.ac.uk/projects/aasp-d-case-metrics
+The rest of matlab files can be used to test the code. Some of the files are taken from https://soundsoftware.ac.uk/projects/aasp-d-case-metrics
 
 Two submissions were sent to the challenge, one uses hardcoded SVM parameters, the other does grid search:
 
 classify_scenes(tmp_path, train_file,test_file, output_file, 0) % use hardcoded parameters for SVM  
 classify_scenes(tmp_path, train_file,test_file, output_file, 1) % use grid search 
 
-The temp_path is used to store features.
+temp_path is used to store features.