TY - GEN
T1 - Underwater sound classification based on Gammatone filter bank and Hilbert-Huang transform
AU - Zeng, Xiangyang
AU - Wang, Shuguang
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/15
Y1 - 2014/12/15
N2 - The variable acoustic environment makes it harder for the application of underwater sound recognition system. However, human auditory system has remarkable ability on dealing with complex acoustic conditions. A robust underwater noise target classification system is expected if this ability can be simulated. Aimed at this purpose, a robust underwater sound classification algorithm which employs Gammatone filter bank and Hilbert-Huang transform is studied in this paper. Gammatone filter bank is used for the simulation of nonlinear dividing of human ears. Then the wavelet denoising procedure is applied on the divided sub-bands. At last, Hilbert-Huang transform is used as the time-frequency analysis tool for the feature extraction. With the help of Hilbert-Huang transform, instantaneous features are extracted, and then used for the built of feature vector. Experimental results indicated the expected efficiency of the proposed algorithm.
AB - The variable acoustic environment makes it harder for the application of underwater sound recognition system. However, human auditory system has remarkable ability on dealing with complex acoustic conditions. A robust underwater noise target classification system is expected if this ability can be simulated. Aimed at this purpose, a robust underwater sound classification algorithm which employs Gammatone filter bank and Hilbert-Huang transform is studied in this paper. Gammatone filter bank is used for the simulation of nonlinear dividing of human ears. Then the wavelet denoising procedure is applied on the divided sub-bands. At last, Hilbert-Huang transform is used as the time-frequency analysis tool for the feature extraction. With the help of Hilbert-Huang transform, instantaneous features are extracted, and then used for the built of feature vector. Experimental results indicated the expected efficiency of the proposed algorithm.
KW - Gammatone filter bank
KW - Hilbert-Huang transform
KW - Time-frequency analysis
UR - https://www.scopus.com/pages/publications/84922682188
U2 - 10.1109/ICSPCC.2014.6986287
DO - 10.1109/ICSPCC.2014.6986287
M3 - 会议稿件
AN - SCOPUS:84922682188
T3 - 2014 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2014
SP - 707
EP - 710
BT - 2014 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2014
Y2 - 5 August 2014 through 8 August 2014
ER -