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Titlebook: Deep Learning Based Speech Quality Prediction; Gabriel Mittag Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive l

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發(fā)表于 2025-3-21 18:32:15 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Deep Learning Based Speech Quality Prediction
編輯Gabriel Mittag
視頻videohttp://file.papertrans.cn/265/264573/264573.mp4
概述Presents how to apply deep learning methods for the task of speech quality prediction.Includes a model that outperforms traditional speech quality models.Presents an in-depth analysis and comparison o
叢書名稱T-Labs Series in Telecommunication Services
圖書封面Titlebook: Deep Learning Based Speech Quality Prediction;  Gabriel Mittag Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive l
描述This book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent advancements in machine learning can be leveraged for the task of speech quality prediction and provides an in-depth analysis of the suitability of different deep learning architectures for this task. The author then shows how the resulting model outperforms traditional speech quality models and provides additional information about the cause of a quality impairment through the prediction of the speech quality dimensions of noisiness, coloration, discontinuity, and loudness..
出版日期Book 2022
關(guān)鍵詞Machine learning; deep learning; speech quality; quality of experience; quality of service
版次1
doihttps://doi.org/10.1007/978-3-030-91479-0
isbn_softcover978-3-030-91481-3
isbn_ebook978-3-030-91479-0Series ISSN 2192-2810 Series E-ISSN 2192-2829
issn_series 2192-2810
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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3D Clock Routing for Pre-bond Testabilityures are divided into three different stages. First, a frame-based neural network calculates features for each time step. The resulting feature sequence is then modelled by a time-dependency neural network. Finally, a pooling stage aggregates the sequence of features over time to estimate the overal
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發(fā)表于 2025-3-22 10:44:54 | 只看該作者
Magda Mostafa,Ruth Baumeister,Martin Tamke dimensions are presented: Noisiness, Coloration, Discontinuity, and Loudness. The resulting dimension scores serve as degradation decomposition and help to understand the underlying reason for a low MOS score. The subjective ground truth values of these scores are perceptual speech quality dimensio
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發(fā)表于 2025-3-22 16:09:41 | 只看該作者
https://doi.org/10.1007/978-3-031-36302-3nd truth MOS that are the target values of the supervised learning approach. In particular, it is common practice to use multiple datasets for training and validation, as subjective data is usually sparse due to the costs that experiments involve. However, these datasets often come from different la
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Power Delivery Network Design for 3D IClity prediction models is motivated with a summary of current state-of-the-art speech quality prediction models and their drawbacks. The two objectives and four research questions that are further investigated in this book are then presented with a following outline of the book.
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