Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1142
Title: Digital Preservation and Noise Reduction using Machine Learning
Authors: Aravinda, K. P
Sandeepa, K. G. H
Sedara, V. V
Chamodya, A. K. Y. L
Dharmasena, T
Abeygunawardhana, P. K. W
Keywords: Digital Preservation
Noise Reduction
Machine Learning
Internal and External Noise
Issue Date: 9-Dec-2021
Publisher: IEEE
Citation: A. K. P., S. K. G. H., S. V. V., C. A. K. Y. L., T. Dharmasena and P. K. W. Abeygunawardhana, "Digital Preservation and Noise Reduction using Machine Learning," 2021 3rd International Conference on Advancements in Computing (ICAC), 2021, pp. 181-186, doi: 10.1109/ICAC54203.2021.9671137.
Series/Report no.: 2021 3rd International Conference on Advancements in Computing (ICAC);Pages 181-186
Abstract: This paper proposes a digital preservation solution for Sinhala audios to conserve those as documents with noise reduction. The solution has implemented multiple noise reduction techniques as a pre-processing step to remove unwanted internal and external noises. A two-step, two-way noise reduction process is applied to produce clean audios based on Deep Convolutional Neural Network (DCNN) and adaptive filter-based techniques. This approach implements two separate noise reduction models for internal and external noises. After that, the speech recognition decoder recognizes the speech and converts it to a Unicode document by acoustic, language, and pronunciation models using extracted audio features from the denoised audio. Further, noise reduction models are decoupled from the preservation solution and exposed as a sub solution for multilingualism noise reduction, supporting English and Sinhala audios.
URI: http://rda.sliit.lk/handle/123456789/1142
ISBN: 978-1-6654-0862-2
Appears in Collections:Research Papers - Dept of Computer Systems Engineering
Research Papers - SLIIT Staff Publications

Files in This Item:
File Description SizeFormat 
Digital_Preservation_and_Noise_Reduction_using_Machine_Learning.pdf
  Until 2050-12-31
1.93 MBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.