Soft computing based feature selection for environmental sound classifcation

The topic of this thesis work is soft computing based feature selection for environmental sound classification. Environmental sound classification systems have a wide range of applications, like hearing aids devices, handheld devices and auditory protection devices. Sound classification systems typically extract features which are learnt by a classifier. Using too many features can result in reduced performance by making the learning algorithm to learn wrong models. The proper selection of features for sound classification is a non-trivial task. Soft computing based feature selection methods are not studied for environmental sound classification, whereas these methods are very promising, because these can handle uncertain information in a more ecient way, using simple set theoretic functions and because these methods are more close to perception based reasoning. Therefore this thesis investigates different feature selection methods, including soft computing based feature selection and classical information, entropy and correlation based approaches. Results of this study show that rough set neighborhood based method performs best in terms of number of features selected, recognition rate and consistency of performance. Also the resulting classification system performs robustly in presence of reverberation