Convolutional Feature Vectors and Support Vector Machine for Animal Sound Classification

Abstract

Pattern classification based on deep network outperforms conventional methods in many tasks. However, if the database for training exhibits internal representation that lacks substantial discernibility for different classes, the network is considered that learning is essentially failed. Such failure is evident when the accuracy drops sharply in the experiments performing classification task where the animal sounds are observed similar. To address and remedy the learning problem, this paper proposes a novel approach composed of a combination of multiple CNNs each separately pre-trained for generating midlevel features according to each class and then merged into a combined CNN unit with SVM for overall classification. For experiment, animal sound database that include 3 classes with 102 species is firstly established. From the experimental results using the database, the proposed method is shown to outperform over prominent conventional methods.