Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhang, Zhongyi
dc.description.abstractSpiking 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.en_US
dc.format.extent79 p.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleFeasibility study of spiking neural network for voice classificationen_US
dc.contributor.supervisorGoh Wang Lingen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Electronics)en_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
  Restricted Access
Main article3.2 MBAdobe PDFView/Open

Page view(s)

Updated on Jul 20, 2024

Download(s) 50

Updated on Jul 20, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.