Optimization of amplitude modulation features for low-resource acoustic scene classification

Abstract

We developed a new feature extraction algorithm based on the Amplitude Modulation Spectrum (AMS), which mainly consists of two filter bank stages composed of low-order recursive filters. The passband range of each filter was optimized by using the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES). The classification task was accomplished by a Linear Discriminant Analysis (LDA) classifier. To evaluate the performance of the proposed acoustic scene classifier based on AMS features, we tested it with the publicly available dataset provided by the IEEE AASP Challenge 2013. Using only 9 optimized AMS features, we achieved 85 % classification accuracy, outperforming the best previously available approaches by 10 %.