Kernel multi-metric learning for multi-channel transient acoustic signal classification

In this paper, we propose a kernel multi-metric learning algorithm for multi-channel transient acoustic signal classification. The proposed method learns a set of metrics jointly for multi-channel transient acoustic signals in a kernel-induced feature space to exploit the non-linearity of the data for improving the classification performance. An effective algorithm is developed for the task of learning multiple metrics in the kernel space. By learning the multiple metrics jointly within a single unified optimization framework, we can learn better metrics to integrate the multiple channels of the signal for a joint classification. Experimental results compared with classical as well as recent algorithms on real-world acoustic datasets verified the effectiveness of the proposed method.