Material based acoustic signal classification - A subspace-based approach

In this paper, approaches to develop subspace-based classifiers for material based signal classification are presented. Material based signal classification is essential in many applications for the identification of the cause of the acoustic signal. Four approaches are presented in this paper for classification. They are namely, classification via eigen-filter characteristics, eigenvector projection method, classification through eigen-filter and eigen-filter bank outputs. For these methods, the optimum order of the eigen-filters is selected based on the maximum true positive counts. The optimal eigenvectors that are most suitable for classification are selected by minimizing the cross-correlation among different classes. Finally, the robustness and the superior performance of the proposed techniques are highlighted by comparing them with time and frequency domain techniques such as AR modeling, spectrogram based classification.