We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound "richer" and "fuller," using a combination of bass extension and dynamic range compression. We also developed an audio energy reduction algorithm for loudspeaker power management by suppressing signal energy below the masking threshold. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. We also designed the circuits to implement the AdaBoost-based analog classifier.

Traditionally, connection management applications (referred to as patchbays) for high-speed audio networking, are predominantly developed using third-generation languages such as C, C# and C++. Due to the rapid increase in distributed audio/video network usage in the world today, connection management applications that control signal routing over these networks have also evolved in complexity to accommodate more functionality. As the result, high-speed audio networking application developers require a tool that will enable them to develop complex connection management applications easily and within the shortest possible time. In addition, this tool should provide them with the reliability and flexibility required to develop applications controlling signal routing in networks carrying real-time data. High-speed audio networks are used for various purposes that include audio/video production and broadcasting. This investigation evaluates the possibility of using Adobe Flash Professional 8, using ActionScript 2.0, for developing connection management applications. Three patchbays, namely the Broadcast patchbay, the Project studio patchbay, and the Hospitality/Convention Centre patchbay were developed and tested for connection management in three sound installation networks, namely the Broadcast network, the Project studio network, and the Hospitality/Convention Centre network. Findings indicate that complex connection management applications can effectively be implemented using the Adobe Flash IDE and ActionScript 2.0.

Esta dissertação apresenta um estudo comparativo de diferentes métodos computacionais de segmentação estrutural musical, onde o principal objetivo é delimitar fronteiras de seções musicais em um sinal de áudio, e rotulá-las, i.e. agrupar as seções encontradas que correspondem a uma mesma parte musical. São apresentadas novas propostas para segmentação estrutural nãosupervisionada, incluindo métodos para processamento em tempo real, alcançando resultados com taxas de erro inferiores a 12%. O método utilizado compreende um estudo dos descritores sonoros e meios de modelá-los temporalmente, uma exposição das técnicas computacionais de segmentação estrutural e novos métodos de avaliação dos resultados que penalizam tanto a incorreta detecção das fronteiras quanto o número incorreto de rótulos encontrados. O desempenho de cada técnica computacional é calculado utilizando diferentes conjuntos de descritores sonoros e os resultados são apresentados e analisados tanto quantitativa quanto qualitativamente. / A comparative study of different music structural segmentation methods is presented, where the goal is to delimit the borders of musical sections and label them, i.e. group the sections that correspond to the same musical part. Novel proposals for unsupervised segmentation are presented, including methods for real-time segmentation, achieving expressive results, with error ratio less then 12%. Our method consists of a study of sound descriptors, an exposition of the computational techniques for structural segmentation and the description of the evaluation methods utilized, which penalize both incorrect boundary detection and incorrect number of labels. The performance of each technique is calculated using different sound descriptor sets and the results are presented and analysed both from quantitative and qualitative points-of-view.