Voice quality assessment in mobile devices considering different fading models

Affonso, Emmanuel T.; Rodríguez, Demóstenes Zegarra; Rosa, Renata Lopes; Andrade, Thiago; Bressan, Graça

doi:10.1109/isce.2016.7797329

Cited by 16 publications

(8 citation statements)

References 1 publication

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“…In a VoIP communication, end-user’s QoE is determined by the user’s perception [ 10 , 11 , 12 ]. In general, speech quality assessment methods can be divided in subjective and objective methods.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Routing Algorithm Based on Reinforcement Machine Learning—A Case of VoIP Service

Militani

Moraes

Rosa

et al. 2021

Sensors

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The routing algorithm is one of the main factors that directly impact on network performance. However, conventional routing algorithms do not consider the network data history, for instances, overloaded paths or equipment faults. It is expected that routing algorithms based on machine learning present advantages using that network data. Nevertheless, in a routing algorithm based on reinforcement learning (RL) technique, additional control message headers could be required. In this context, this research presents an enhanced routing protocol based on RL, named e-RLRP, in which the overhead is reduced. Specifically, a dynamic adjustment in the Hello message interval is implemented to compensate the overhead generated by the use of RL. Different network scenarios with variable number of nodes, routes, traffic flows and degree of mobility are implemented, in which network parameters, such as packet loss, delay, throughput and overhead are obtained. Additionally, a Voice-over-IP (VoIP) communication scenario is implemented, in which the E-model algorithm is used to predict the communication quality. For performance comparison, the OLSR, BATMAN and RLRP protocols are used. Experimental results show that the e-RLRP reduces network overhead compared to RLRP, and overcomes in most cases all of these protocols, considering both network parameters and VoIP quality.

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Section: Introductionmentioning

confidence: 99%

Enhanced Routing Algorithm Based on Reinforcement Machine Learning—A Case of VoIP Service

Militani

Moraes

Rosa

et al. 2021

Sensors

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“…For improving the angular resolution of LF images, the view synthesis can be performed, and Sub-Aperture Images (SAIs) can be synthesized from a set of input views extracted from the plenoptic cameras. Some view synthesis studies [23,24] break down the view synthesis into the disparity estimator and a color predictor is performed by a Convolutional Neural Network (CNN) [25][26][27][28]. These studies obtain better results than other methods [29,30] that require depth information for view warping and image registration.…”

Section: Introductionmentioning

confidence: 99%

Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

et al. 2021

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Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

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“…The path delays and signal power variations are example of problems at communication in wireless network. These degradation factors originate the fading [15]. Most of research on speech quality focus on wired network parameters, such as packet losses, jitter and delays; however, the wireless channel impairment characteristics and techniques used in wireless communication systems are not related with the speech quality [16], [17].…”

Section: Introductionmentioning

confidence: 99%

A Speech Quality Classifier based on Tree-CNN Algorithm that Considers Network Degradations

Vieira

Rosa

Rodríguez

2020

J. commun. softw. syst. (Online)

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Many factors can affect the users’ quality of experience (QoE) in speech communication services. The impairment factors appear due to physical phenomena that occur in the transmission channel of wireless and wired networks. The monitoring of users’ QoE is important for service providers. In this context, a non-intrusive speech quality classifier based on the Tree Convolutional Neural Network (Tree-CNN) is proposed. The Tree-CNN is an adaptive network structure composed of hierarchical CNNs models, and its main advantage is to decrease the training time that is very relevant on speech quality assessment methods. In the training phase of the proposed classifier model, impaired speech signals caused by wired and wireless network degradation are used as input. Also, in the network scenario, different modulation schemes and channel degradation intensities, such as packet loss rate, signal-to-noise ratio, and maximum Doppler shift frequencies are implemented. Experimental results demonstrated that the proposed model achieves significant reduction of training time, reaching 25% of reduction in relation to another implementation based on DRBM. The accuracy reached by the Tree-CNN model is almost 95% for each quality class. Performance assessment results show that the proposed classifier based on the Tree-CNN overcomes both the current standardized algorithm described in ITU-T Rec. P.563 and the speech quality assessment method called ViSQOL.

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Voice quality assessment in mobile devices considering different fading models

Cited by 16 publications

References 1 publication

Enhanced Routing Algorithm Based on Reinforcement Machine Learning—A Case of VoIP Service

Enhanced Routing Algorithm Based on Reinforcement Machine Learning—A Case of VoIP Service

Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

A Speech Quality Classifier based on Tree-CNN Algorithm that Considers Network Degradations

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