Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149298
Title: The study of attention mechanism in sound classification
Authors: Goh, Jonathan Jun Yu
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Goh, J. J. Y. (2021). The study of attention mechanism in sound classification. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149298
Abstract: In modern society today, high rise building and motor vehicles exist all around us. The robust sound events detected within the environment can be heard clearly by us. However, this might not be the case for machines. I.e. Robots. Sounds captured by machines, are often filled with interference from the environment. As a result, when specific sounds such as breaking of glass or even car honks are detected, machines are unable to identify the sound event accurately. This created the idea of developing an automated sound classifier using the deep-learning model. By leveraging on the attention mechanism used in transformers in combination with CNN, the new system allows multiple sound events to be attended at the same time which promotes a parallel computation approach as compared to other neural networks such as the Recurrent Neural Network (RNN) and Long-short term memory (LSTM) which suffers from a series computation approach. Overall, The performances of the network models are evaluated based on different parameters to get a comparison of which network models is best suited for time-series dataset.
URI: https://hdl.handle.net/10356/149298
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
The Study Of Attention Mechanism In Sound Classification.pdf
  Restricted Access
2.99 MBAdobe PDFView/Open

Page view(s)

78
Updated on May 16, 2022

Download(s)

6
Updated on May 16, 2022

Google ScholarTM

Check

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