TY - GEN
T1 - Transient acoustic signal classification using joint sparse representation
AU - Zhang, Haichao
AU - Nasrabadi, Nasser M.
AU - Huang, Thomas S.
AU - Zhang, Yanning
PY - 2011
Y1 - 2011
N2 - In this paper, we present a novel joint sparse representation based method for acoustic signal classification with multiple measurements. The proposed method exploits the correlations among the multiple measurements with the notion of joint sparsity for improving the classification accuracy. Extensive experiments are carried out on real acoustic data sets and the results are compared with the conventional discriminative classifiers in order to verify the effectiveness of the proposed method.
AB - In this paper, we present a novel joint sparse representation based method for acoustic signal classification with multiple measurements. The proposed method exploits the correlations among the multiple measurements with the notion of joint sparsity for improving the classification accuracy. Extensive experiments are carried out on real acoustic data sets and the results are compared with the conventional discriminative classifiers in order to verify the effectiveness of the proposed method.
KW - joint sparse recovery
KW - Joint sparsity classification
KW - sparse representation
UR - http://www.scopus.com/inward/record.url?scp=80051651960&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2011.5946922
DO - 10.1109/ICASSP.2011.5946922
M3 - 会议稿件
AN - SCOPUS:80051651960
SN - 9781457705397
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2220
EP - 2223
BT - 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
T2 - 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Y2 - 22 May 2011 through 27 May 2011
ER -