*2.4. Classification*

The classification stage employs a multiclass SVM. The basic idea of SVM is to find a hyperplane that separates D-dimensional data into its two classes [38]. SVM is a discriminative model for classification that principally depends on two basic assumptions. First, complex classification problems can be classified through simple linear discriminative functions by transforming data into a high-dimension space. Second, the training samples for SVMs consist only of those data points that lie close to the decision surface, with the supposition that they provide the most relevant information for classification [39]. SVMs were originally proposed as binary classifiers. However, in real scenarios, data is to be classified into multiple classes. This is done by using multiclass SVM. Either a one-against-one (OAO) or one-against-all (OAA) approach can be used [40]. For acoustic scene classification setup proposed in this work, the joint feature vector extracted from previous stage is used to train the multiclass SVM OAO classifier.
