Browse > Article
http://dx.doi.org/10.5657/KFAS.2016.0214

Time-Frequency Feature Extraction of Broadband Echo Signals from Individual Live Fish for Species Identification  

Lee, Dae-Jae (Division of Marine Production System Management, Pukyong National University)
Kang, Hee-Young (Hydrographic Survey Division, Korea Hydrographic and Oceanographic Administration)
Pak, Yong-Ye (Silla Co., Ltd)
Publication Information
Korean Journal of Fisheries and Aquatic Sciences / v.49, no.2, 2016 , pp. 214-223 More about this Journal
Abstract
Joint time-frequency images of the broadband acoustic echoes of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The acoustic features were extracted by changing the sliced window widths and dividing the time window by a 0.02-ms interval and the frequency window by a 20-kHz bandwidth. The 22 spectrum amplitudes obtained in the time and frequency domains of the SPWVD images were fed as input parameters into an artificial neural network (ANN) to verify the effectiveness for species-dependent features related to fish species identification. The results showed that the time-frequency approach improves the extraction of species-specific features for species identification from broadband echoes, compare with time-only or frequency-only features. The ANN classifier based on these acoustic feature components was correct in approximately 74.5% of the test cases. In the future, the identification rate will be improved using time-frequency images with reduced dimensions of the broadband acoustic echoes as input for the ANN classifier.
Keywords
Time-frequency feature extraction; SPWVD; Broadband echo signals; Artificial neural network;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Demuth H, Beale M and Hagan M. 2009. Neural Network ToolboxTM 6 User’s Guide. The MathWorks Inc, Massachusetts, USA, 84-226.
2 Dong Y and Cui Y. 2012. Analysis of a new joint time-frequency distribution of suppressing cross-term. Res J Appl Sci Eng Technol 4, 1580-1584.
3 Fassler SMM, Fernandes PG, Semple SIK and Brierley AS. 2009. Depth-dependent swimbladder compression in herring Clupea haengus observed using magnetic resonance imaging. J Fish Bio 74, 296-303. http://dx.doi.org/10.1111/j.1095-8649.2008.02130.x.   DOI
4 Gavrovska AM, Paskas MP and Reljin IS. 2010. Determination of morphologically characteristics PCG segments from spectrogram image. Teflor J 2, 74-77.
5 Lee DJ, Kang HY and Kwak MS. 2015. Analysis and classification of broadband acoustic echoes from live individual fish using pulse compression technique. Korean J Fish Aquat Sci 48, 207-220. http://dx.doi.org/10.5657/KFAS.2015.0207.   DOI
6 Han SK and Kim HT. 2010. Efficient radar target recognition using a combination of range profile and time-frequency analysis. Progress Electrom Res 108, 131-141.   DOI
7 Jaffe JS. 2006. Using multi-angle scattered sound to size fish swimbladders. ICES J Mar Sci 63, 1397-1404. http://dx.doi.org/10.1016/j.icesjms.2006.04.024.   DOI
8 Kuruvilla J and Gunavathi K. 2014. Lung cancer classification using neural networks for CT images. Computer Methods Programs Biomedicine 113, 202-209.   DOI
9 Lee DJ. 2015a. Time-frequency analysis of broadband acoustic scattering from chub mackerel Scomber japonicas, goldeye rockfish Sebastes thompsoni, and fat greenling Hexagrammos otakii. Korean J Fish Aquat Sci 48, 221-232. http://dx.doi.org/10.5657/KFAS.2015.0221.   DOI
10 Lee DJ. 2015b. Changes in the orientation and frequency dependence of target strength due to morphological differences in the fish swim bladder. Korean J Fish Aquat Sci 48, 233-243. http://dx.doi.org/10.5657/KFAS.2015.0233.   DOI
11 Nesse TL, Hobek H and Korneliussen RJ. 2009. Measurement of acoustic-scattering spectra from the whole and pars of Atlantic mackerel. ICES J Mar Sci 66, 1169-1175. http://dx.doi.org/ 10.1093/icesjms/fsp087.   DOI
12 Safizadeh MS, Lepine A, Forsyth DS and Fahr A. 2001. Time-frequency analysis of pulsed eddy current signals. J Nondestruct Evaluat 20, 73-86. http://dx.doi.org/10.1023/A:1012244208475.   DOI
13 Shilbayeh NF, Alwakeel MM and Naser MM. 2013. An efficient neural network for recognition gestural Hindi digits. American J Appl Sci 10, 938-951.   DOI
14 Shui PL, Shang HY and Zhao YB. 2007. Instantaneous frequency estimation based on directionally smoothed pseudoWegner-Ville distribution bank. IET Radar Sonar Navig 1, 317-325. http://dx.doi.org/10.1049/rsn:20060123.   DOI
15 Stanton TK, Chu D, Jech JM and Irish JD. 2010. New broadband methods for resonance classification and high-resolution imagery of fish with swimbladders using a modified commercial broadband echosounder. ICES J Mar Sci 67, 365-378. http://dx.doi.org/10.1093/icesjms/fsp262.   DOI
16 Traykovski LVM, O’Driscoll RL and McGehee DE. 1998. Effect of orientation on broadband acoustic scattering of Atlantic krill Euphausia superb: Implications for inverting zooplankton spectral acoustic signatures for angle of orientation. J Acoust Soc Am 104, 2121-2135.   DOI
17 Foote KG. 1980. Importance of the swimbladder in acoustic scattering by fish: A Comparison of gadoid and mackerel target strengths. J Acoust Soc Am 67, 2084-2089.   DOI