標題: Titlebook: Deep Learning Based Speech Quality Prediction; Gabriel Mittag Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive l [打印本頁] 作者: 萬靈藥 時間: 2025-3-21 18:32
書目名稱Deep Learning Based Speech Quality Prediction影響因子(影響力)
書目名稱Deep Learning Based Speech Quality Prediction影響因子(影響力)學(xué)科排名
書目名稱Deep Learning Based Speech Quality Prediction網(wǎng)絡(luò)公開度
書目名稱Deep Learning Based Speech Quality Prediction網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning Based Speech Quality Prediction被引頻次
書目名稱Deep Learning Based Speech Quality Prediction被引頻次學(xué)科排名
書目名稱Deep Learning Based Speech Quality Prediction年度引用
書目名稱Deep Learning Based Speech Quality Prediction年度引用學(xué)科排名
書目名稱Deep Learning Based Speech Quality Prediction讀者反饋
書目名稱Deep Learning Based Speech Quality Prediction讀者反饋學(xué)科排名
作者: 附錄 時間: 2025-3-21 22:57 作者: 懶惰人民 時間: 2025-3-22 04:10
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作者: cinder 時間: 2025-3-22 05:50 作者: 靈敏 時間: 2025-3-22 10:44
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作者: 不能仁慈 時間: 2025-3-22 16:09
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作者: 不能仁慈 時間: 2025-3-22 19:07 作者: 彎彎曲曲 時間: 2025-3-22 22:00 作者: Peak-Bone-Mass 時間: 2025-3-23 02:08 作者: FEIGN 時間: 2025-3-23 09:14
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.作者: Dealing 時間: 2025-3-23 10:32 作者: RODE 時間: 2025-3-23 15:58 作者: BUST 時間: 2025-3-23 19:11
978-3-030-91481-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 退出可食用 時間: 2025-3-24 01:51
Gabriel MittagPresents 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作者: Asparagus 時間: 2025-3-24 03:37
T-Labs Series in Telecommunication Serviceshttp://image.papertrans.cn/d/image/264573.jpg作者: Comedienne 時間: 2025-3-24 06:57 作者: 龍卷風(fēng) 時間: 2025-3-24 14:28
Bias-Aware Loss for Training from Multiple Datasets,nted that considers the biases between different datasets by learning the biases automatically during training. The calculated loss is adjusted in a way that errors, which only occur due to dataset-specific biases, are not considered when optimising the weights of the quality prediction neural netwo作者: deactivate 時間: 2025-3-24 18:46 作者: reception 時間: 2025-3-24 20:10
Quality Assessment of Transmitted Speech,. Then the terms “speech quality” and “speech quality dimensions” are introduced, and subjective speech quality assessment methods are discussed. Afterwards, a review of instrumental speech quality prediction models is given. At first, traditional speech quality models are presented. Then machine le作者: Audiometry 時間: 2025-3-25 03:10
Neural Network Architectures for Speech Quality Prediction,ures 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作者: 淡紫色花 時間: 2025-3-25 07:00
Double-Ended Speech Quality Prediction Using Siamese Networks,ded model of the previous “Neural Network Architectures” chapter but calculates a feature representation of the reference and the degraded signal through a Siamese CNN with Time-Dependency modelling network that shares the weights between both signals. The resulting features are then used to align t作者: A保存的 時間: 2025-3-25 08:15 作者: 英寸 時間: 2025-3-25 12:45
Bias-Aware Loss for Training from Multiple Datasets,nd 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作者: 密切關(guān)系 時間: 2025-3-25 16:41
NISQA: A Single-Ended Speech Quality Model, previous chapters. Overall, the model is trained and evaluated on a wide variety of 78 different datasets. To train a model that delivers robust speech quality estimation for unknown speech samples, it is important to use speech samples that are highly diverse and come from different sources (i.e. 作者: Cultivate 時間: 2025-3-25 22:58 作者: Badger 時間: 2025-3-26 00:30
Quality Assessment of Transmitted Speech,arning based models from literature, which are not based on deep learning, are described. Finally, a brief overview of deep learning architectures and deep learning based speech quality models is given.作者: Minutes 時間: 2025-3-26 07:33
Neural Network Architectures for Speech Quality Prediction,l speech quality. It will be shown that the combination of a CNN for per-frame modelling, a self-attention network for time-dependency modelling, and an attention-pooling network for pooling yields the best overall performance.作者: 精確 時間: 2025-3-26 11:04
NISQA: A Single-Ended Speech Quality Model,data distributions). Because of this, in addition to newly created datasets for this work, also speech datasets from the POLQA pool, the ITU-T P Suppl. 23 pool, and further internal datasets are used. The model is then finally evaluated on a live-talking test dataset that contains recordings of real phone calls.作者: 他去就結(jié)束 時間: 2025-3-26 13:15 作者: Encapsulate 時間: 2025-3-26 19:13 作者: Myocarditis 時間: 2025-3-27 00:53
Book 2022 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 pr作者: idiopathic 時間: 2025-3-27 03:14
Prediction of Speech Quality Dimensions with Multi-Task Learning,elp to understand the underlying reason for a low MOS score. The subjective ground truth values of these scores are perceptual speech quality dimensions. Because the model aims to predict the overall MOS and additionally dimension scores from the same speech signal, the prediction can be seen as a Multi-Task-Learning (MTL) problem.作者: 消耗 時間: 2025-3-27 08:37 作者: 人充滿活力 時間: 2025-3-27 11:23 作者: implore 時間: 2025-3-27 16:46 作者: anaphylaxis 時間: 2025-3-27 18:30 作者: 征稅 時間: 2025-3-27 23:43
3D IC Cooling with Micro-Fluidic Channelson for the time-alignment problem that occurs for speech signals transmitted through VoIP networks and shows how the clean reference signal can be incorporated into speech quality models that are based on end-to-end trained neural networks.作者: Explosive 時間: 2025-3-28 04:37 作者: 純樸 時間: 2025-3-28 09:52 作者: 動機 時間: 2025-3-28 10:40 作者: scotoma 時間: 2025-3-28 16:55
2192-2810 g 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..978-3-030-91481-3978-3-030-91479-0Series ISSN 2192-2810 Series E-ISSN 2192-2829 作者: 瑪瑙 時間: 2025-3-28 20:31
Maurizio Gasperiniated p38 stabilizes (Mancini and Di Battista, Inflamm Res 60:1083–1092, 2011) COX-2 mRNA and upregulates expression of IL-beta (Bachstetter and Van Eldik, Aging Dis 1:199–211, 2010) probably in a similar manner, inhibiting p38 appeared to be a way of blocking TNF-alpha, COX-2, and IL-beta simultaneo作者: Urgency 時間: 2025-3-29 01:44
