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The topic of this 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…mehr

Produktbeschreibung
The topic of this 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 efficient way, using simple set theoretic functions and because these methods are more close to perception based reasoning. Therefore this research investigates different feature selection methods, including soft computing based feature selection and classical information, entropy and correlation based approaches.
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Autorenporträt
Aamir Shakoor holds a MSc in Electrical Engineering and in Electronics. His research interests include Autonomous Vehicles, Deep Learning, AI and Decision Making for Autonomous Drive. Currently, he is working as a System Verification Engineer at Volvo Group Truck Technology.