Machine learning methods for signal, image and speech processing /

The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear...

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Bibliographic Details
Online Access: Full text (MCPHS users only)
Main Author: Jabbar, M. A. (Author)
Other Authors: Prasad, Kantipudi MVV (Editor), Peng, Sheng-Lung, Bin Ibne Reaz, Mamun (Editor), Madureira, Ana
Format: Electronic eBook
Language:English
Published: Aalborg : River Publishers, 2021
Series:River Publishers series in signal, image and speech processing.
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Local Note:ProQuest Ebook Central
Description
Summary:The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains.
Item Description:6.1.3 Improved Sensing of Cognitive Radio for MB pectrum using Wavelet Filtering.
Physical Description:1 online resource (258 pages)
ISBN:9788770223683
8770223688
9781003338789
100333878X
9781000794748
1000794741
9781000791624
1000791629
Source of Description, Etc. Note:Print version record.
Biographical or Historical Data:M.A. Jabbar, MVV Prasad Kantipudi, Sheng-Lung Peng, Mamun Bin Ibne Reaz, Ana Maria Madureira