Abstract

Spiking Neural Network (SNN) uses spike which is a discrete event that occurs in time, as opposed to continuous time signals. This allows low cost resources implementation in electronic devices because the spikes occurs in particular time events. In this research, we implement and test various spike encoding techniques of mel frequency cepstrum designed for automatic speech recognition system with SNN method. The implementation is done in the Xilinx Pynq-Z2 Field Programmable Gate Array (FPGA). This Pynq-Z2 is equipped with an ARM Processor that will be used as the whole computational process. The preprocessing audio signal uses Mel Spectogram method which is the output spectrogram and translated into spike trains the will serve as input to a SNN. Results show that the latency spike encoding method results in the least amount of spikes. Substantial memory and power savings from spike encoding of audio signals has been shown and can potentially be used as the input stage for a low-cost hardware implementation of a speech recognition system.

Original languageEnglish
Title of host publicationProceedings - 2022 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
EditorsTeguh Prakoso, Munawar Agus Riyadi, M. Arfan, Yosua Alvin Adi Soetrisno, Hadha Afrisal
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages81-85
Number of pages5
ISBN (Electronic)9781665471480
DOIs
Publication statusPublished - 2022
Event9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022 - Semarang, Indonesia
Duration: 25 Aug 202226 Aug 2022

Publication series

NameProceedings - 2022 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022

Conference

Conference9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
Country/TerritoryIndonesia
CitySemarang
Period25/08/2226/08/22

Keywords

  • MFCC
  • SNN
  • Spectogram

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