Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78456
Title: Feasibility study of spiking neural network for voice classification
Authors: Zhang, Zhongyi
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: Spiking neural network (SNN), largely inspired by a biological neural network, offers many advantages over the conventional deep neural network (DNN) [1]/ convolutional neural network (CNN) [2] in energy efficiency and latency due to its event based processing, which makes it a good candidate for energy efficient voice classification. Furthermore, the learning mechanism of the SNN typically requires only local information of pre-synaptic neuron and post-synaptic neuron when a spike happens, providing a light-weighted energy-efficient and hardware-friendly solution for the applications of voice recognition and classification. This dissertation reports a digital spiking neuron based on Leaky Integrate-and-Fire (LIF) model [3]. As the basic units of neurons in SNN, several LIF models jointly construct a 3-layer neural network. Subsequently, eight Infinite Impulse Response (IIR) digital filters ranging from 0 Hz to 400 Hz are designed to analyze human voice in both time and frequency domains, which extracts the feature of male and female voices. Two thousands male and female voice clips are used as training sets and five hundred voices are used as test sets in the neural network. The functionality and performance of the proposed digital spiking neuron can be verified by test sets to recognize male and female voice. The obtained results and simulations in MATLAB demonstrate the superiority of the proposed SNN and determine the potential of such systems in voice classification.
URI: http://hdl.handle.net/10356/78456
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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