A Comprehensive Solar Powered Remote Monitoring and Identification of Houston Toad Call Automatic Recognizing Device System Design
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The Houston Toad is an endangered amphibian living in the edge of extinction. To save from annihilation, localization of their mating calls needs to be determined in order to protect the eggs from being hunted by predators. The current method of monitoring their vocalization lacks real-time and onboard voice recognition capability. In this research, a solar-based battery powered Raspberry Pi is designed along with a microphone that records environmental sound at prescribed intervals triggered by a Witty Pi module. This thesis proposes a naive approach to build a predictor model to detect the Houston Toad mating call signature through recorded audio files from the embedded design. These prerecorded several audio files have been analyzed to determine the unique characteristics of Houston toad call for their identification. The audio file is bandpass filtered, and then preprocessed by multiplying every frame with the hamming window into segments. Next, the Mel-Filterbank and Mel-Frequency Spectral Coefficient (MFCC) are used for feature extraction, and the Support Vector Machine (SVM) and Multi-layer Perceptron (MLP) neural networks are utilized as classifiers to determine the best fit. This experimental result reflects the higher accuracy of the MLP neural network over SVM showing the best potential of classification. The trained neural network predictor model is deployed in the Raspberry Pi to identify Houston Toad, and if there exists trailing in the call, it sends notification of time stamp via Email and SMS over the GSM and GPRS modules to the researchers.