Publisher : The Korean Society of Fisheries and Aquatic Science
DOI : 10.5657/KFAS.2016.0224
Title & Authors
Acoustic Identification of Six Fish Species using an Artificial Neural Network Lee, Dae-Jae;
The objective of this study was to develop an artificial neural network (ANN) model for the acoustic identification of commercially important fish species in Korea. A broadband echo acquisition and processing system operating over the frequency range of 85-225 kHz was used to collect and process species-specific, time-frequency feature images from six fish species: black rockfish Sebastes schlegeli, black scraper Thamnaconus modesutus [K], chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, konoshiro gizzard shad Konosirus punctatus and large yellow croaker Larimichthys crocea. An ANN classifier was developed to identify fish species acoustically on the basis of only 100 dimension time-frequency features extracted by the principal components analysis (PCA). The overall mean identification rate for the six fish species was 88.5%, with individual identification rates of 76.6% for black rockfish, 82.8% for black scraper, 93.8% for chub mackerel, 90.6% for goldeye rockfish, 96.9% for konoshiro gizzard shad and 90.6% for large yellow croaker, respectively. These results demonstrate that individual live fish in well-controlled environments can be identified accurately by the proposed ANN model.
Fish species identification;Time-frequency image;Artificial neural network;Principal components analysis;Confusion matrix;
Bai Y, Zhang H and Hao Y. 2009. The performance of the backpropagation algorithm with varying slope of the activation function. Chaos, Solutions Fractals 40, 69-77.
Clay CS and Horne JK. 1994. Acoustic models of fish: The Atlantic cod (Gadus morhua). J Acoust Soc Am 96, 1161-1668.
Demuth H, Beale M and Hagan M. 2009. Neural Network ToolboxTM 6 User’s Guide. The MathWorks Inc, Massachusetts, USA, 84-226.
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.
Fassler SMM, Fernandes PG, Semple SIK and Brierley AS. 2009. Depthe-dependent swimbladder compression in herring Clupea haengus obserbed using magnetic resonance imaging. J Fish Bio 74, 296-303. http://dx.doi.org/10.1111/j.1095-8649.2008.02130.x.
Fernandes PG. 2009. Classification trees for species identification of fish-school echotraces. ICES J Mar Sci 66, 1073-1080. http://dx.doi.org/10.1093/icesjms/fsp060.
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.
Gavrovska AM, Paskas MP and Reljin IS. 2010. Determination of morphologically characteristic PCG segments from spectrogram image. Teflor J 2, 74-77.
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.
Imberger J and Boashash B. 1986. Application of the Wigner-Ville distribution to temperature gradient microstructure: A new technique to study small-scale variations. J Physic Oceanography 16, 1997-2012.
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.
Kuruvilla J and Gunavathi K. 2014. Lung cancer classification using neural networks for CT images. Computer Methods Programs Biomedicine 113, 202-209.
Latha P, Ganesan L and Annadurai S. 2009. Face recognition using neural networks. Signal Processing: An International J 3, 153-160.
Lee DJ, Kang HY and Kwak MS. 2015. Analysis and classification of broadband acoustic echoes from individual live fish using the pulse compression technique. Korean J Fish Aquat Sci 48, 207-220. http://dx.doi.org/10.5657/KFAS.2015.0207.
Lee DJ. 2015a. Time-frequency analysis of broadband acoustic scattering from chub mackerel Scomber japonicas, goldeye rockfish Sebestes thompsoni, and fat greenling Hexagrammos otakii. Korean J Fish Aquat Sci 48, 221-232. http://dx.doi.org/10.5657/KFAS.2015.0221.
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.
Lee DJ, Kang HY and Pak YY. 2016. Time-frequency feature extraction of broadband echo signals from individual live fish for species identification. Kor J Fish Aquat Sci 49, 214-223. http://dx.doi.org/10.5657/KFAS.2016.0214.
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.
Pinjare SL and Arun Kumar M. 2012. Implementation of neural network back propagation training algorithm on FPGA. International J Computer Appl 52, 1-7.
Saad MHM, Nor MJM, Bustami FRA and Ngadiran R. 2007. Classification of heart abnormalities using artificial neural network. J Appl Sci 7, 820-825.
Santo RdE. 2012. Principal component analysis applied to digital image compression. Eistein 10, 135-139.
Shilbayeh NF, Alwakeel MM and Naser MM. 2013. An efficient neural network for recognition gestural Hindi digits. American J Appl Sci 10, 938-951.
Shui PL, Shang HY and Zhao YB. 2007. Instantaneous frequency estimation based on directionally smoothed pseudo-Wegner-Ville distribution bank. IET Radar Sonar Navig 1, 317-325. http://dx.doi.org/10.1049/rsn:20060123.
Simmons EJ, Armstong F and Copland PJ. 1996. Species identification using wideband backscattering with neural network and discriminant analysis. ICES J Mar Sci 53, 189-195.
Stanton TK, Chu D, Jech JM and Irish JD. 2010. New broad-band 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.
Tsagarakis K, Giannoulaki, M, Pyrounaki M and Machias A. 2015. Species identification of small pelagic fish schools by means of hydroacoustics in the Eastern Mediterranean Sea. Medit Mar Sci 16, 151-161. http://dx.doi.org/10.12681/mms.799.
Woillez M, Ressler PH and Wilson CD. 2012. Multifrequency species classification of acoustic- trawl survey data using semi-supervised learning with class discovery. J Acoust Soc Am 131, EL184-EL190. http://dx.doi.org/10.1121/1.3678685.
Zuo W, Zhang D and Wang K. 2006. Bidirectional PCA with assembled matrix distance metric for image recognition. IEEE Trans Sys Man Cyber 36, 863-872.