基于特征融合的海洋哺乳动物声音识别

doi:10.16180/j.cnki.issn1007-7820.2019.05.007

Abstract

Abstract:

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

ZHONG Mingtuo,CAI Wenyu. Marine Mammal Sound Recognition Based on Feature Fusion[J].Electronic Science and Technology, 2019, 32(5): 32-37.

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References 19

[1]	David E Hanny , et al. Marine mammal acoustic detections in the northeastern Chukchi Sea, September 2007-July 2011[J]. Continental Shelf Research, 2013(67):127-146.
[2]	Nanaware S, Shastri R, Joshi Y, et al. Passive acoustic detection and classification of marine mammal vocalizations[C]. Lucknow:International Conference on Communication and Signal Processing, 2014.
[3]	André M, Van d S M, Zaugg S , et al. Listening to the Deep: live monitoring of ocean noise and cetacean acoustic signals.[J]. Marine Pollution Bulletin, 2011,63(1-4):18-26. doi: 10.1016/j.marpolbul.2011.04.038
[4]	Ibrahim A K, Zhuang H, Erdol N, et al. A New approach for north atlantic right whale upcall detection[C]. Xi’an: International Symposium on Computer,Consumer and Control, 2016.
[5]	Brown J C, Smaragdis P . Hidden Markov and Gaussian mixture models for automatic call classification[J]. Journal of the Acoustical Society of America, 2009, 125(6):EL221.
[6]	Suleman M, Tamaki Ura. Vocalization based individual classification of humpback whales using support vector machine[C]. Aberdeen:Oceans, 2007.
[7]	Dugan P J, Rice A N, Urazghildiiev I R, et al. North atlantic right whale acoustic signal processing: Part I. comparison of machine learning recognition algorithms[C]. Long Island:Applications and Technology Conference, 2010.
[8]	Zhou X, Garcia-Romero D, Duraiswami R, et al. Linear versus mel frequency cepstral coefficients for speaker recognition[C]. Hawaii:Automatic Speech Recognition and Understanding, 2011.
[9]	Harma A. Automatic identification of bird species based on sinusoidal modeling of syllables[C]. Hong Kong:IEEE International Conference on Acoustics,Speech and Signal Processing, 2003.
[10]	宋知用 . MATLAB在语音信号分析与合成中的应用[M]. 北京: 北京航空航天大学出版社, 2013.
	Song Zhiyong. Application of MATLAB in speech signal analysis and synthesis[M]. Beijing: Beihang University Press, 2013.
[11]	张帅林 . 改进的智能家居语音关键词识别算法[J]. 电子科技, 2017,30(7):5-8.
	Zhang Shuailin . Improved speech keyword spotting algorithm in smart home[J]. Electronic Science and Technology, 2017,30(7):5-8.
[12]	李艳, 成凌飞, 张培玲 . 一种基于改进谱熵的语音端点检测方法[J]. 计算机科学, 2016,43(S2):233-236.
	Li Yan , Cheng lingfei, Zhang Peiling. Speech endpoint detection based on improved spectral entropy[J]. Computer Science, 2016,43(S2):233-236.
[13]	张冬松, 马琪 . 一种基于SVM的负载识别算法[J]. 电子科技, 2017,30(8):59-62.
	Zhang Dongsong, Ma Qi . A load identification algorithm based on SVM[J]. Electronic Science and Technology, 2017,30(8):59-62.
[14]	余清清, 李应, 李勇 . 基于SVM模型的自然环境声音的分类[J]. 计算机与数字工程, 2010,38(7):1-5.
	Yu Qingqing, Li Ying, Li Yong . A SVM-based classification approach for natural sounds[J]. Computer & Digital Engineering, 2010,38(7):1-5.
[15]	SanDiego University of California. Voices in the sea[EB/OL]. ( 2015- 07- 27)[2018-04-02].
[16]	The Cornell Lab of Ornithology.Macaulay library[EB/OL]. ( 2014- 09- 28)[2018-04-02].
[17]	Gingras B, Fitch W T . A three-parameter model for classifying anurans into four genera based on advertisement calls[J]. Journal of the Acoustical Society of America, 2013,133(1):547-59. doi: 10.1121/1.4768878
[18]	Oswald M, Oswald J N, Lammers M O , et al. Integration of real time odontocete call classification algorithm into PAMGUARD signal processing software[J]. Journal of the Acoustical Society of America, 2011,129(4):2639-2639.
[19]	González-Hernández F R, Sánchez-Fernández L P, Suárez-Guerra S , et al. Marine mammal sound classification based on a parallel recognition model and octave analysis[J]. Applied Acoustics, 2017,119(3):17-28. doi: 10.1016/j.apacoust.2016.11.016

数据集	特征	分类器	训练时间/s	识别率/%±std
海洋哺乳动物音频(61种,407个样本)	MFCC	KNN	2.02	87.88%±7.89%
		RF	3.49	89.04%±4.55%
		SVM	2.57	90.07%±3.23%
	LFCC	KNN	2.10	89.74%±9.97%
		RF	3.53	91.01%±6.64%
		SVM	2.71	91.53±5.02%
	MFCC+LFCC	KNN	4.79	93.94%±5.73%
		RF	6.11	93.81%±6.76%
		SVM	5.01	94.48±4.03%
	MFCC+LFCC+ SE+SD	KNN	4.86	94.88%±7.04%
		RF	5.91	94.91%±5.95%
		SVM	5.07	95.67%±4.96%

参考文献	特征参数	原文识别种类	识别率/% ±std
[4]	MFCC+DWT	1	91.29%±5.29%
[19]	倍频程	11	89.38%±6.86%
本文算法	MFCC+LFCC +SE+SD	61	95.67%±4.96%

Marine Mammal Sound Recognition Based on Feature Fusion

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