Design of analog audio classifiers with AdaBoost-Based feature selection

The design of analog classifiers constitutes a trade- off between performance and complexity, and designers have historically adopted more complex architectures to lower the error rate of a classification task. An alternative design paradigm is presented in this paper: We design the front-end of a sound classification system with simple "base" classifiers. We then enhance the overall performance with the aid of the AdaBoost algorithm, which selects the most appropriate "base" classifiers and combines them with different weights. We describe the general architecture and the algorithm to select features and present a design example with simulation results in a TSMC- compatible 0.35-μm technology.