Buradasınız

Anasayfa » Content

SU ALTI AKUSTİK SİNYAL TANIMA YÖNTEMLERİ

UNDERWATER ACOUSTIC SIGNAL RECOGNITION METHODS

Journal Name:

  • Deniz Harp Okulu Deniz Bilimleri ve Mühendisliği Dergisi

Publication Year:

  • 2009

Key Words:

Keywords (Original Language):

Author Name
Abstract (2. Language): 
The term Underwater Acoustic Signal Recognition (UASR) is used for identifying the platforms by some techniques from the acoustic sound signals they produce. In this paper, we propose two different schemes for UASR. In both schemes, the feature extraction is performed using Mel-Frequency Cepstral Coefficients and Linear Predictive Coding derived Cepstral Coefficients which have been extensively utilized in speech recognition. In the first scheme, the features extracted frame by frame are used as a sequence in the representation of the whole signal. The classification of that sequence of vectors is then performed by Hidden Markov Models with various topologies. The second scheme represents the frame features using Bag of Acoustic Words approach. In training stage, all the feature vectors extracted from the input signal are first clustered into a set of acoustic words. Each feature vector is then assigned to an acoustic word. After the frequency of each word is calculated in the input signal, the final representation is performed by the co-occurrence list of the acoustic words.
Bookmark/Search this post with
Abstract (Original Language): 
Su altı Akustik Sinyal Tanıma (SAST) terimi, platformları ürettikleri seslerden bazı teknikler kullanarak tanıma işlemi için kullanılmaktadır. Her gemi makine, pervane, tekne yapısı ve mürettebat alışkanlıklarının birleşiminden meydana gelen kendine özgü özelliğe sahiptir. Bu makalede, SAST için iki değişik yöntem önermekteyiz. Her iki yöntemde özellik çıkarımı işlemi konuşma tanıma konusunda yarar sağladığı kanıtlanmış Mel-Frekans Kepstral Katsayıları ve Doğrusal Kestirimci Kodlama ile türetilmiş Kepstral Katsayılar ile hesaplanmaktadır. İlk yöntem de öznitelik çıkarımından sonra sinyal vektör dizisi olarak ifade edilir. Vektör dizilerinin sınıflandırılması daha sonra değişik topolojilere sahip Saklı Markov Modelleri ile yapılmaktadır. İkinci yöntem çerçeve özelliklerini Akustik ses kümesi yaklaşımını kullanarak temsil eder. Eğitme safhasında, giriş sinyalinin çerçevelerinden çıkarılan tüm öznitelik vektörleri önce bir akustik kelimeler kümesine gruplandırılır. Öznitelik vektörlerinin her biri bir akustik kelimeye atanmaktadır.
FULL TEXT (PDF): 
PDF icon arastirmax-su-alti-akustik-sinyal-tanima-yontemleri.pdf
  • 3
64-78
  • English

REFERENCES

References: 

[1] Lobo, V., F. M. Pires, ”Ship Noise Classification Using Kohonen Networks”, EANN 95,
1995.
[2] Kurcan, R. Serdar, “Isolated Word Recognition from in-Ear Microphone Data Using
Hidden Markov Models (Hmm)”, NPS Master Thesis, 2006.
[3] Nooralahiyan, A., H. Kirby, “Vehicle Classification by Acoustic Signature”, Elsevier
Science Ltd., 1998.
[4] Lin, Jing, “Feature Extraction of Machine Sound Using Wavelet and Its Application in
Fault Diagnosis”, Elsevier Science Ltd., 2001.
[5] Bennett, Richard Campbell. ”Classification of Underwater Signals Using a BACKPropagation
Neural Network”, NPS Master Thesis, 1997.
[6] Halkias C., Daniel P., “Estimating the Number of Marine Mammals Using Recordings
of Clicks from One Microphone”, Columbia University, 2006
[7] Urazghildiiev, I., C.W. Clark, T. Krein, “Acoustic Detection and Recognition of Fin
Whale and North Atlantic Right Whale Sounds”, Bioacoustics Research Prog., Cornell
Laboratory of Ornithology, 2008.
[8] Alkan, Mahmut. “Warship Sound Signature Recognition Using Mel Frequency Cepstral
Coefficients”, Naval Science and Engineering Institute MS Thesis, 2005.
[9] Yue, Z., K. Wei, X. Qing, “A Novel Modelling and Recognition Method for
Underwater Sound Based on HMT in Wavelet Domain”, Springer, Verlag Berlin
Heidelberg, 2004.
[10] Rashidul, H., M. Jamil, G. Rabbani, S. Rahman, “Speaker Identification Using Mel
Frequency Cepstral Coefficients”, 3rd International Conference on Electrical & Computer
Engineering, Dhaka, 2004.
[11] Bardici, N., B. Skarin, "Speech Recognition using Hidden Markov Model", Blekinge
Institute of Technology MS Thesis, 2006.
[12] Vergin, R., “An Algorithm for Robust Signal Modeling in Speech Recognition,” IEEE
International Conference on Acoustics, Speech, and Signal Processing (ICASSP '98), 1998.
[13] Vaseghi, Saeed V., “Advanced Digital Signal Processing and Noise Reduction”,
Second Edition, John Wiley & Sons Ltd., 2000
[14] Rabiner, L. R., R. W. Schafer, “Digital Processing of Speech Signals”, Prentice-Hall,
Englewood Cliffs, New Jersey, 1978.
[15] Picone, J. “Signal Modeling Techniques in Speech Recognition,” Proceedings of the
IEEE, 1993.
[16] Wikipedia, the free encyclopedia web site, 2007, (last accessed on 16 July 2009)
[17] Ayats A.R., “Object Recognition for Autonomous Robots: Comparison of two
approaches”, Report of the stay at the Autonomous Systems Lab, 2007.
[18] Rabiner, L.R., “A Tutorial on Hidden Markov Models and Selected Applications in
Speech Recognition,” IEEE, 1989.
[19] Gold, B., N. Morgan, “Speech and Audio Signal Processing,” John Wiley & Sons,
2000.
[20] Deller, J. R., J. Hansen, J. Proakis, “Discrete-Time Processing of Speech Signals”,
IEEE Press, New York, 2000.
[21] Baum L.E., T. Petrie, “Statistical inference for probabilistic functions of finite state
Markov chains,” Annals of Mathematical Statistics, 1966.
[22] Baum, L.E., T. Petrie, G. Soules, “A maximization technique in the statistical analysis
of probabilistic functions of Markov chains,” Annals of Mathematical Statistics, 1970.
[23] RABINER L. R., B-H. Juang, “Fundamentals of Speech Recognition”, Prentice Hall,
1993.
[24] Kil, D.H., F. Shin, “Pattern Recognition and Prediction with Applications to Signal
Processing (Modern Acoustics and Signal Processing)”, 1998.
[25] Arica, N., Fatos. T.Y., “A Shape Descriptor Based on Circular Hidden Markov
Model”, Department of Computer Engineering, METU, 2000
[26] Arica, N., Fatos. T.Y., “A New HMM Topology for Shape Recognition”, Department
of Computer Engineering, METU, 1995.
[27] Teknomo, K., “K Nearest Neighbors Tutorial”, 2006, people.revoledu.com/, (last
accessed on 16 July 2009)
[28] Grudic,G. Nearest Neighbor Learning lecture notes, 2005, www.cs.colorado.edu (last
accessed on 16 July 2009)
[29] Vapnik, V., Golowich S., Smola A., ”Support Vector Method for Function
Approximation, Regression Estimation, and Signal Processing”, Cambridge, 1997.
[30] Temko A., C. Nadeu, “Classification of Acoustic Events Using SVM-Based Clustering
Schemes”, Universitat Politècnica de Catalunya, 2005.
[31] Jakkula, Vikramaditya, “Tutorial on Support Vector Machine (SVM)”, School of
EECS, Washington State University, 2006.

Thank you for copying data from http://www.arastirmax.com