Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (5): 32-37.doi: 10.16180/j.cnki.issn1007-7820.2019.05.007

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Marine Mammal Sound Recognition Based on Feature Fusion

ZHONG Mingtuo,CAI Wenyu   

  1. School of Electronic Information,Hangzhou Dianzi University,Hangzhou 310018,China
  • Received:2018-05-16 Online:2019-05-15 Published:2019-05-06
  • Supported by:
    Zhejiang Provincial Natural Science Foundation of China(LY18F030006)


In order to improve the recognition rate and robustness of marine mammal sound recognition algorithm, this paper proposed a method for sound recognition by using the fusion of MFCC,LFCC and temporal features as feature parameters. This method enhanced the characterization ability of different frequency bands by fusing different cepstral coefficients and described the sound information more comprehensively by integrating the temporal features. To be specific, each continuous marine mammal recording was first preprocessed into individual syllables. Then, cepstral coefficients and temporal features were calculated from each syllable. Finally, the fused features were identified by support vector machine. Unlike traditional algorithms which only recognized few mammals, this method was tested in a sample database containing 61 marine mammal sounds. The test results showed that the proposed algorithm improved the recognition rate by 5.5% compared with the traditional MFCC, and had a better recognition performance in the low SNR environment.

Key words: feature fusion, marine mammals, sound recognition, support vector machine, cepstral coefficients, temporal features

CLC Number: 

  • TN912.34