Acoustic Design in Urban Development : analysis of urban soundscapes and acoustic ecology research in New York City

The world is urbanizing rapidly with more than half of the global population now living in cities. Improving urban environments for the well-being of the increasing number of urban citizens is becoming one of the most important challenges of the 21st century. Even though it is common that city planners have visions of a ’good urban milieu’, those visions are concerning visual aesthetics or practical matters. The qualitative perspective of sound, such as sonic diversity and acoustic ecology are neglected aspects in architectural design. Urban planners and politicians are therefore largely unaware of the importance of sounds for the intrinsic quality of a place. Whenever environmental acoustics is on the agenda, the topic is noise abatement or noise legislation – a quantitative attenuation of sounds. Some architects may involve acoustical aspects in their work but sound design or acoustic design has yet to develop to a distinct discipline and be incorporated in urban planning.My aim was to investigate to what extent the urban soundscape is likely to improve if modern architectural techniques merge with principles of acoustics. This is an important, yet unexplored, research area. My study explores and analyses the acoustical aspects in urban development and includes interviews with practitioners in the field of urban acoustics, situated in New York City. My conclusion is that to achieve a better understanding of the human living conditions in mega-cities, there is a need to include sonic components into the holistic sense of urban development.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.