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Title: Auditory Representation Learning
Researcher: Sailor, Hardik B.
Guide(s): Patil, Hemant A.
Keywords: Engineering and Technology,Computer Science,Computer Science Artificial Intelligence
University: Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)
Completed Date: 2018
Abstract: Representation learning (RL) or feature learning has a huge impact in the field newlineof signal processing applications. The goal of the RL approaches is to learn the newlinemeaningful representation directly from the data that can be helpful to the pattern newlineclassifier. Specifically, the unsupervised RL has gained a significant interest in newlinethe feature learning in various signal processing areas including the speech and newlineaudio processing. Recently, various RL methods are used to learn the auditory like newlinerepresentations from the speech signals or its spectral representations. In this thesis, we propose a novel auditory representation learning model based on the Convolutional Restricted Boltzmann Machine (ConvRBM). The auditory like sub band filters are learned when the model is trained directly on the raw newlinespeech and audio signals with arbitrary lengths. newline
Pagination: xxv, 215p.
Appears in Departments:Department of Information and Communication Technology

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01_title.pdfAttached File84.02 kBAdobe PDFView/Open
02_declaration and certificate.pdf82.31 kBAdobe PDFView/Open
03_acknowledgements.pdf60.45 kBAdobe PDFView/Open
04_table of contents.pdf129.74 kBAdobe PDFView/Open
05_abstract.pdf59.1 kBAdobe PDFView/Open
06_list of principal symbols and acronyms.pdf124.86 kBAdobe PDFView/Open
07_list of tables and figures.pdf243.97 kBAdobe PDFView/Open
08_chapter 1.pdf922.84 kBAdobe PDFView/Open
09_chapter 2.pdf3.09 MBAdobe PDFView/Open
10_chapter 3.pdf1.59 MBAdobe PDFView/Open
11_chapter 4.pdf1.65 MBAdobe PDFView/Open
12_chapter 5.pdf3.86 MBAdobe PDFView/Open
13_chapter 6.pdf2.26 MBAdobe PDFView/Open
14_chapter 7.pdf1.06 MBAdobe PDFView/Open
15_chapter 8.pdf120.2 kBAdobe PDFView/Open
16_appendix.pdf466.56 kBAdobe PDFView/Open
17_references.pdf135.25 kBAdobe PDFView/Open

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