Environmental Sound Classification using Hybrid Ensemble Model

Abstract

Environmental sound classification (ESC) is the most trending research areas. The sounds in the surroundings such as screaming, air conditioners, and rain droplets can help in the development of context-aware applications. It is complex to process the envi- ronmental sounds as compared to speech and music due to the unstructured essence of environmental sounds. In the past, certain preprocessing techniques, feature extraction, and classification algorithms are used for ESC. Several researchers have applied ma- chine learning classifiers for ESC and certain ensemble classifiers are also used but the accuracy can be increased if instead of combining homogeneous classifiers, heterogeneous classifiers can be ensembled. In this paper, a hybrid ensemble classifier is used for ESC on the UrbanSound8k dataset and cepstral features Mel Frequency Cepstral Coefficients are used. Five different machine learning classifiers- Decision Tree, Support Vector Machine, Logistic Regression, K- Nearest Neighbour, and Naive Bayes are used to develop a hybrid ensemble model. The highest accuracy is obtained when all the five classifiers are combined. The proposed approach gives an accuracy of 79.4% and is compared with the benchmark results using individual classifiers and the former out- performs the latter. The results of the hybrid ensemble model on the UrbanSound8K dataset are also compared with the dataset ESC-10.