URBAN SOUND RECOGNITION USING DIFFERENT FEATURE EXTRACTION TECHNIQUES

Abstract

The application of the advanced methods for noise analysis in the urban areas through the development of systems for classification of sound events significantly improves and simplifies the process of noise assessment. The main purpose of sound recognition and classification systems is to develop algorithms that can detect and classify sound events that occur in the chosen environment, giving an appropriate response to their users. In this research, a supervised system for recognition and classification of sound events has been established through the development of feature extraction techniques based on digital signal processing of the audio signals that are further used as an input parameter in the machine learning algorithms for classification of the sound events. Various audio parameters were extracted and processed in order to choose the best set of parameters that result in better recognition of the class to which the sounds belong. The created acoustic event detection and classification (AED/C) system could be further implemented in sound sensors for automatic control of environmental noise using the source classification that leads to reduced amount of required human validation of the sound level measurements since the target noise source is evidently defined.