Sparsity-Basd Representation fo Classifucation Algorithms and Comparsion Results for Transient Acoustic Signals

Report No. ARL-TR-7677
Authors: Minh Dao and Tung_Duong Tran-Luu
Date/Pages: May 2016; 52 pages
Abstract: In this report, we propose a general sparsity-based framework for the classification of transient acoustic signals; this framework enforces various sparsity structures like joint-sparse or group-and-joint-sparse within measurements of multiple acoustic sensors. We further robustify our models to deal with the presence of dense and large but correlated noise and signal interference (i.e., low-rank interference). Another contribution is the implementation of deep learning architectures to perform classification on the transient acoustic data set. Extensive experimental results are included in the report to compare the classification performance of sparsity-based and deep-network-based techniques with conventional classifiers such as Markov switching vector auto-regression, Gaussian mixture model, support vector machine (SVM), hidden Markov model (HMM), sparse logistic regression, and the combination of SVM and HMM methods (SVM-HMM) for 2 experimental sets of 4-class and 6-class classification problems.
Distribution: Approved for public release
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Last Update / Reviewed: May 1, 2016