Low-power audio classification for ubiquitous sensor networks

In the past researchers have proposed a variety of features that are based on the human auditory system. However none of these features have been able to replace mel-frequency cepstral coefficients (MFCCs) as the preferred feature for audio classification problems, either because of computational costs involved or because of their poor performance in the presence of noise. In this paper we present new features derived from a model of the early auditory system. We compare the performance of the new features with MFCC in a four-class audio classification problem and show that they perform better. We also test the noise robustness of the new features in a two-way audio classification problem and show that it outperforms the MFCCs. Further, these new features can be implemented in low-power analog VLSI circuitry making them, ideal for low-power sensor networks.