caa a22 4500 606205349 CHVBK 20210128101006.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00034-014-9953-8 doi (NATIONALLICENCE)springer-10.1007/s00034-014-9953-8 A Robust Feature Extraction Algorithm for the Classification of Acoustic Targets in Wild Environments [Elektronische Daten] [Jingchang Huang, Shiliang Xiao, Qianwei Zhou, Feng Guo, Xing You, Haiyan Li, Baoqing Li] The acoustic recognition technology of unattended ground sensor systems applied in wild environments is faced with the challenge of complicated and strong acoustic noise, especially wind noise. Moreover, the commonly used Mel-frequency cepstral coefficients (MFCCs) are sensitive to noise interference. To resolve the problem, a robust feature extraction method, called harmonic Mel-frequency cepstral coefficients (HMFCCs), is proposed for acoustic target classification. By combining an acoustic signal's harmonic model with the MFCC method, the HMFCC has the ability to emphasize the signals emitted by the principal acoustic components of the target. In the experiment conducted for this study, three data sets are sampled under the same conditions, except for wind power levels. Then the classifier, which is trained by one of the three data sets, is used to recognize the others data sets. According to the experimental results, the HMFCC-based classification accuracies of the three data sets are higher than those of other state-of-the-art methods, indicating that HMFCC is a kind of noise-insensitive feature. Springer Science+Business Media New York, 2015 Robust nationallicence Feature extraction nationallicence Classification nationallicence Harmonic nationallicence Acoustic target nationallicence Harmonic Mel-frequency cepstral coefficients (HMFCC) nationallicence Huang Jingchang Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut Xiao Shiliang Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut Zhou Qianwei Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut Guo Feng Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut You Xing Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut Li Haiyan School of Information Science and Engineering, Yunnan University, 650091, Kunming, China aut Li Baoqing Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/7(2015-07-01), 2395-2406 0278-081X 34:7<2395 2015 34 34 https://doi.org/10.1007/s00034-014-9953-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-014-9953-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Huang Jingchang Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut NATIONALLICENCE 700 1- Xiao Shiliang Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut NATIONALLICENCE 700 1- Zhou Qianwei Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut NATIONALLICENCE 700 1- Guo Feng Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut NATIONALLICENCE 700 1- You Xing Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut NATIONALLICENCE 700 1- Li Haiyan School of Information Science and Engineering, Yunnan University, 650091, Kunming, China aut NATIONALLICENCE 700 1- Li Baoqing Shanghai Institute of Microsystems and Information Technology, CAS, 200050, Shanghai, China aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/7(2015-07-01), 2395-2406 0278-081X 34:7<2395 2015 34 34