Bio acoustic signal identification based on sparse representation classifier frog species voice identification
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Date
2018-06
Authors
Wan, Zhi Xuan
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Abstract
Most insects and animal produce sounds as a way of communication within their species or as
noises resulting from feeding or travelling. Automated recognition of bio-acoustic signals is
becoming vital in the aspect of biological research or environmental monitoring. With the
improvement of technology, scientists today are able to classify types and species of animals
by their vocalizations without even need to see the animal or insects with naked eye. Hence,
species identification based on their calls or vocalization is an important topic to enhance in
the aspect of ecological research. This project aims to develop a frog species voice
identification system, recognizing different frog species through analyzing their calls. In the
data acquisition stage, databases from Intelligent Biometric Research Group (IBG), School of
Electrical and Electronics Engineering, Universiti Sains Malaysia in collaboration with School
of Pharmacy, Universiti Sains Malaysia have been used to evaluate the performance of the
system. Raw frog call files are processed using Mel-Frequency Cepstral Coefficient (MFCC)
technique to extract features that will be needed in testing and training the system. In this
project, the classifier used is Sparse Representation Classifier (SRC) and Kernel Sparse
Representation Classifier (KSRC). Performance between SRC and KSRC is compared and
discussed in this project. Besides, a graphic user interface (GUI) is also developed to facilitate
the user while interacting with the system. Two experiments were done in this project, both
using SRC and KSRC. In short, KSRC (96.6667%) has a higher performance in accuracy
compared to SRC (95.6667%). However, KSRC takes a longer computation time compared to
SRC. A GUI was developed implementing KSRC with feature dimension of 64-by-64 as an
outcome of this project.