Normalized approximate descent used for spike based automatic bird species recognition system

Abstract

The paper involves a study on the application of spiking neural network (SNN), which is also known as the third generation of neural networks for automatic bird species recognition. Spiking neural network has worked incredibly well in Neuromorphic computing, such as sequence identification, character identification, etc. This neural network architecture is a consistent model, which comprises of sensory encrypt, training and decrypt functional parts. This decrypt layer utilizes an algorithm based on the normalized approximate gradient descent (NormAD) for synaptic weight adjustment. The result reflected in this paper is for processing spatiotemporal pattern, and has higher computational power over perceptron, along with biological plausibility of spiking neurons. The proposed representation uses segmentation of audio frame based on the energy of the bird call separating voiced, unvoiced and a silence portion of it. This is passed to the attribute extrication method i.e. Mel frequency Cepstral coefficient and the by-product is thus fed to the SNN system for bird species classification. The research output recognizes 14 bird species with 90.10% accuracy in comparison to the perceptron system with an accuracy of 78%.