Bio Acoustic Signal Identification Based On Sparse Representation Classifier Frog Species Voice Identification
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 technol...
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| Main Author: | |
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| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2018
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| Online Access: | http://eprints.usm.my/53308/1/Bio%20Acoustic%20Signal%20Identification%20Based%20On%20Sparse%20Representation%20Classifier%20Frog%20Species%20Voice%20Identification_Wan%20Zhi%20Xuan_E3_2018.pdf http://eprints.usm.my/53308/ |
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| Summary: | 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. |
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