Audio Event Classification for Urban Soundscape Analysis

The study of urban soundscapes has gained momentum in recent years as more people become concerned with the level of noise around them and the negative impact this can have on comfort. Monitoring the sounds present in a sonic environment can be a laborious and time–consuming process if performed manually. Therefore, techniques for automated signal identification are gaining importance if soundscapes are to be objectively monitored.
This thesis presents a novel approach to feature extraction for the purpose of classifying urban audio events, adding to the library of techniques already established in the field. The research explores how techniques with their origins in the encoding of speech signals can be adapted to represent the complex everyday sounds all around us to allow accurate classification.
The analysis methods developed herein are based on the zero–crossings information contained within a signal. Originally developed for the classification of bioacoustic signals, the codebook of Time–Domain Signal Coding (TDSC) has its band–limited restrictions removed to become more generic. Classification using features extracted with the new codebook achieves accuracies of over 80% when combined with a Multilayer Perceptron classifier.
Further advancements are made to the standard TDSC algorithm, drawing inspiration from wavelets, resulting in a novel dyadic representation of time–domain features. Carrying the label of Multiscale TDSC (MTDSC), classification accuracies of 70% are achieved using these features.
Recommendations for further work focus on expanding the library of training data to improve the accuracy of the classification system. Further research into classifier design is also suggested.