1. Classification and Recognition of Underwater Target Based on MFCC Feature Extraction
- Author
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Yizhou Ge, Yuze Tong, and Xin Zhang
- Subjects
Human ear ,Computer science ,business.industry ,Feature vector ,05 social sciences ,Feature extraction ,050801 communication & media studies ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,Radiation ,Noise ,0508 media and communications ,0202 electrical engineering, electronic engineering, information engineering ,Spectral analysis ,Artificial intelligence ,Mel-frequency cepstrum ,Underwater ,business - Abstract
The key to underwater target recognition is to extract the effective features of underwater target radiation noise. This paper presents an effective method for underwater target recognition and classification by extracting Mel-Frequency Cepstral Coefficients (MFCCs) features of underwater target radiation noise. Compared with traditional spectral analysis methods, MFCC makes full use of the non-linear auditory effect of the human ear with different perception capabilities for sounds of different frequencies. In this paper, the classification experiment of the radiated noise of the three types of measured underwater targets is done, where the MFCC feature vectors of the three types of targets are extracted, and the K-Nearest Neighbor (K-NN) algorithm is used to classify and identify them. Finally, the experimental results show that the method is effective. more...
- Published
- 2020
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