Abstract
In this study, we have designed a vehicle classification system which classifies Assault Amphibian Vehicle and Dragon Wagon, using acoustic and sesimic features. We implemented Mel Frequency Cepstral Coefficient (MFCC) algorithm to extract features of the acoustic and sesimic data, and these extracted features were reduced by using Vector Quantizaton algorithm. Both Support Vector Machine (SVM) and k-Nearest Neighborhood (k-NN) algorithms were implemented and their classification performances were evaluated.
Keywords
Mel-frequency cepstrum
Support vector machine
Computer science
Identification (biology)
Pattern recognition (psychology)
Speech recognition
Feature extraction
Artificial intelligence
Cepstrum
Subject Areas
Speech Recognition and Synthesis
·Artificial Intelligence ·Physical Sciences
Speech and Audio Processing
·Signal Processing ·Physical Sciences
Gait Recognition and Analysis
·Biomedical Engineering ·Physical Sciences
Citations by Year
2013: 1
2018: 1
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