Document Type



This item is available under a Creative Commons License for non-commercial use only



Publication Details

A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computing 9Data Analytics), January 2019.


Environmental sound is rich source of information that can be used to infer contexts. With the rise in ubiquitous computing, the desire of environmental sound recognition is rapidly growing. Primarily, the research aims to recognize the environmental sound using the perceptually informed data. The initial study is concentrated on understanding the current state-of-the-art techniques in environmental sound recognition. Then those researches are evaluated by a critical review of the literature. This study extracts three sets of features: Mel Frequency Cepstral Coefficients, Mel-spectrogram and sound texture statistics. Two kinds machine learning algorithms are cooperated with appropriate sound features. The models are compared with a low-level baseline model. It also presents a performance comparison between each model with the high-level human listeners. The study results in sound texture statistics model performing the best classification by achieving 45.1% of accuracy based on support vector machine with radial basis function kernel. Another Mel-spectrogram model based on Convolutional Neural Network also provided satisfactory results and have received predictive results greater than the benchmark test.