Remember Me
Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:

OpenAIRE is about to release its new face with lots of new content and services.
During September, you may notice downtime in services, while some functionalities (e.g. user registration, login, validation, claiming) will be temporarily disabled.
We apologize for the inconvenience, please stay tuned!
For further information please contact helpdesk[at]openaire.eu

fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Lampert, Thomas; O'Keefe, Simon (2010)
Languages: English
Types: Article
Subjects: 3102
The detection of tracks in spectrograms is an important step in remote sensing applications such as the analysis of marine mammal calls and remote sensing data in underwater environments. Recent advances in technology and the abundance of data requires the development of more sensitive detection methods. This problem has attracted researchers' interest from a variety of backgrounds ranging between image processing, signal processing, simulated annealing and Bayesian filtering. Most of the literature is concentrated in three areas: image processing, neural networks, and statistical models such as the Hidden Markov model. There has not been a review paper which describes and critically analyses the application of these key algorithms. This paper presents an extensive survey and an algorithm taxonomy, additionally each algorithm is reviewed according to a set of criteria relating to their success in application. These criteria are defined to be their ability to cope with noise variation over time, track association, high variability in track shape, closely separated tracks, multiple tracks, the birth/death of tracks, low signal-to-noise ratios, that they have no a priori assumption of track shape and that they are computationally cheap. Our analysis concludes that none of these algorithms fully meets these criteria. (C) 2009 Elsevier Ltd. All rights reserved.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] W. Koenig, H. K. Dunn, L. Y. Lacy, The sound spectrograph, J. Acoust. Soc. Am. 18 (1) (1946) 244-244.
    • [2] R. P. Morrissey, J. Ward, N. DiMarzio, S. Jarvis, D. J. Moretti, Passive acoustic detection and localisation of sperm whales (Physeter Macrocephalus) in the tongue of the ocean, Applied Acoustics 67 (2006) 1091- 1105.
    • [3] D. K. Mellinger, S. L. Nieukirk, H. Matsumoto, S. L. Heimlich, R. P. Dziak, J. Haxel, M. Fowler, C. Meinig, H. V. Miller, Seasonal occurrence of north atlantic right whale (Eubalaena glacialis) vocalizations at two sites on the scotian shelf, Marine Mammal Science 23 (2007) 856-867.
    • [4] J. M. de Seixas, W. S. Filho, J. B. O. S. Filho, D. O. Damazio, N. N. Moura, A compact online neural system for classifying passive sonar signals, in: Proc. of the International Conference on Signal Processing Applications and Technology, 1999, pp. 1-5.
    • [5] C.-H. Chen, J.-D. Lee, M.-C. Lin, Classification of underwater signals using neural networks, Tamkang J. of Science and Engineering 3 (1) (2000) 31-48.
    • [6] W. S. Filho, J. M. de Seixas, L. P. Caloba, Principle component analysis for classifying passive sonar signals, in: Proc. of the IEEE International Symposium on Circuits and Systems, Vol. 3, 2001, pp. 592-595.
    • [7] S. Yang, Z. Li, X. Wang, Ship recognition via its radiated sound: The fractal based approaches, J. Acoust. Soc. Am. 11 (1) (2002) 172-177.
    • [8] J. Ghosh, K. Turner, S. Beck, L. Deuser, Integration of neural classifiers for passive sonar signals, Control and Dynamic Systems - Advances in Theory and Applications 77 (1996) 301-338.
    • [9] B. P. Howell, S. Wood, S. Koksal, Passive sonar recognition and analysis using hybrid neural networks, in: Proc. of OCEANS '03, Vol. 4, 2003, pp. 1917-1924.
    • [10] Y. Shi, E. Chang, Spectrogram-based formant tracking via particle filters, in: Proc. of IEEE ICASSP, Vol. 1, 2003, pp. I-168-I-171.
    • [11] B. G. Quinn, Estimating frequency by interpolation using Fourier coefficients, IEEE Trans. Signal Process. 42 (5) (1994) 1264-1268.
    • [12] J. Ward, M. Fitzpatrick, N. DiMarzio, D. Moretti, R. Morrissey, New algorithms for open ocean marine mammal monitoring, in: Proc. of OCEANS 2000, Vol. 3, 2000, pp. 1749-1752.
    • [13] S. E. Moore, K. M. Stafford, D. K. Mellinger, J. A. Hildebrand, Listening for large whales in the offshore waters of Alaska, Bioscience 56 (2006) 49-55.
    • [14] I. R. Urazghildiiev, C. W. Clark, Acoustic detection of north atlantic right whale contact calls using spectrogram-based statistics, The Journal of the Acoustical Society of America 122 (2007) 769-776.
    • [15] R. Urick, Principles of Underwater Sound, 3rd Edition, McGraw-Hill, New York, 1983.
    • [20] Y. H. Yang, Relaxation method applied to lofargram, Master's thesis, Naval Postgraduate School Monterey CA, U.S.A. (June 1990).
    • [21] T.-S. Chen, Simulated annealing in sonar track detection, Master's thesis, Naval Postgraduate School Monterey CA, U.S.A. (December 1990).
    • [22] D. C. Rife, R. R. Boorstyn, Single-tone parameter estimation from discrete-time observations, IEEE Transactions on Information Theory 20 (1974) 591-598.
    • [23] R. A. Altes, Detection, estimation, and classification with spectrograms, The Journal of the Acoustical Society of America 67 (1980) 1232-1246.
    • [24] R. F. Barrett, D. R. A. McMahon, ML estimation of the fundamental frequency of a harmonic series, in: Proc. of ISSPA 87, Brisbane, Aurstralia, 1987, pp. 333-336.
    • [25] J. S. Abel, H. J. Lee, A. P. Lowell, An image processing approach to frequency tracking, in: Proc. of the IEEE Int. Conference on Acoustics, Speech and Signal Processing, Vol. 2, 1992, pp. 561-564.
    • [31] A. Khotanzad, J. H. Lu, M. D. Srinath, Target detection using a neural network based passive sonar system, in: International Joint Conference on Neural Networks, Vol. 1, 1989, pp. 335-440.
    • [32] N. Leeming, Artificial neural nets to detect lines in noise, in: International Conference on Acoustic Sensing and Imaging, 1993, pp. 147-152.
    • [33] G. D. Kendall, T. J. Hall, T. J. Newton, An investigation of the generalisation performance of neural networks applied to lofargram classification, Neural Computing and Applications 1 (2) (1993) 147-159.
    • [34] G. J. Adams, R. J. Evans, Neural networks for frequency line tracking, IEEE Transactions on Signal Processing 42 (4) (1994) 936-941.
    • [35] J.-C. D. Martino, B. Colnet, M. D. Martino, The use of non supervised neural networks to detect lines in lofargram, in: Proc. of the IEEE Int. Conference on Acoustics, Speech and Signal Processing, Vol. 2, IEEE, 1994, pp. 293-296.
    • [52] S. J. Nowlan, G. E. Hinton, Simplifying neural networks by soft weightsharing, Neural Computation 4 (4) (1992) 473-493.
    • [53] G. D. Kendall, T. J. Hall, Improving generalisation with Ockham's networks: minimum description length networks, in: Third International Conference on Artificial Neural Networks, 1993, pp. 81-85.
    • [61] M. Arulampalam, S. Maskell, N. Gordon, T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian bayesian tracking, IEEE Transactions on Signal Processing 50 (2) (2002) 174-188.
    • [62] R. F. Barrett, D. A. Holdsworth, Frequency tracking using hidden Markov models with amplitude and phase information, IEEE Transactions on Signal Processing 41 (1993) 2965-2976.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Cookies make it easier for us to provide you with our services. With the usage of our services you permit us to use cookies.
More information Ok