Application of PCA as an acoustical signal processing tool for object classification purposes

• Autorzy: Soszyński P. , Gloza I.
• Języki publikacji: EN

Abstrakty

EN

The authors presented a technique for an optimal representation of acoustical signals for further object classification purposes using different statistical and neural methods. It is based on principal component analysis (PCA) which is a transformation of vectors localized in k-dimensional observation (feature) space into lower n-dimensional component space retaining majority of included information. The resulting improvement in classification efficiency by a chosen statistical classifier was verified by a numerical experiment.

• Wydawca: Polskie Towarzystwo Akustyczne. Oddział Gdański
• Czasopismo: Hydroacoustics
• Rocznik: 2005
• Tom: Vol. 8
• Strony: 193--198
• Opis fizyczny: Bibliogr. 7 poz., rys., tab.

Twórcy

autor

Soszyński P.

• Naval University of Gdynia, Poland Śmidowicza 69, 81-103, Gdynia

autor

Gloza I.

• Naval University of Gdynia, Poland Śmidowicza 69, 81-103, Gdynia

Bibliografia

• [1] Cichocki Andrzej, Amari Shun-ichi, Adaptive Blind Signal And Image Processing, John Wiley & Sons, Ltd. 2002.
• [2] Duda Richard O., Hart Peter E., Stork David G., Pattern Classification, John Wiley & Sons, 2002.
• [3] Härdle Wolfgang, Simar L., Applied Multivariate Statistical Analysis, Springer-Verlag, 2003.
• [4] Pearson, K., On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2:559–572, 1901.
• [5] Soszyński Przemysław, Stateczny Andrzej, The Method of Underwater Object Classification Using Multi-layer Perceptron, Proceedings of the 2nd International Symposium on Hydroacoustics, Gdańsk-Jurata, s. 157-160, 1999.
• [6] Tzanakou Evangelia M., Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence, CRC Press, 1999.
• [7] Webb Andrew R., Statistical Pattern Recognition, Second Edition, John Wiley & Sons, 2002.