Sparsity-based fusion of multiple sensors and multiple features for acoustic transients classification
In this paper, we propose a collaborative multi-sensor sparsity-based framework for the classification of acoustic signals which enforces various sparsity constraints such as row-sparsity and group-sparsity across measurements of a single microphone array as well as across multiple arrays, while simultaneously extracting a low-rank interference term. We further extend our framework to incorporate information at the feature level by imposing a joint-structure among multiple feature spaces. A fast and efficient algorithm based on alternative direction method of multiplier (ADMM) is proposed to solve the optimization problems of the models. Extensive experiments are conducted on real data collected by the U. S. Army Research Laboratory for the purpose of identifying weapons from their firing sounds. The classification results are compared to conventional classifiers and deep-learning-based algorithms to verify the efficacy of the proposed methods.