Daily sound recognition for elderly people using ensemble methods

This paper presents our investigations on automatic daily sound recognition using ensemble methods. Two benchmark datasets RWCP-DB and Sound Dataset are utilized for this purpose. A set of acoustic features for daily sound recognition is identified and used. First, sound classification is carried out using individual classifiers on both datasets. As the classification accuracy comes out lower with base classifiers as compared to the results reported in literature, ensemble methods are then employed for classification task. The ensemble methods prove to be effective and robust in recognizing daily sounds as they yield high recognition rates. The classification accuracies achieved by our proposed setup of ensemble methods are higher than those mentioned in literature for the two daily sound datasets.