Assessment of Conversational Speech Quality Inside Vehicles, Concerning Influences of Room Acoustics and Driving Noises

2012 ◽  
Vol 98 (3) ◽  
pp. 461-474 ◽  
Author(s):  
Oliver Jung
Keyword(s):  
Transfer Functions ◽  
Strong Relationship ◽  
Speech Quality ◽  
Room Acoustics ◽  
Conversational Speech ◽  
Acoustical Parameters ◽  
Vehicle Interior ◽  
Lombard Effect ◽  
Speech Spectrum ◽  
The Impact

This study considers the influences of room acoustics and driving noises in vehicle interiors on the subjectively perceived acoustical quality of conversations between passengers. A listening test with 25 participants was performed inside a laboratory to assess the impact of different vehicle interior transfer functions on the speech quality assessment in four predetermined dimensions. Idealized driving noises at three different vehicle speeds were presented simultaneously with speech samples to quantify the interferences of these noise conditions with varied signal-to-noise ratios. To minimize the influence of different human speakers, four talkers (two male and two female) were selected from commercially available audio books. The respective speech samples were adjusted in level and long-term average speech spectrum to the common values of conversational speech. The automatic reflex of raising one's voice in noisy environments, called “Lombard Effect” [1], was taken into account for an additional adjustment of speech levels while driving noises were present. A strong relationship between the speech-to-noise ratio and the test participants' evaluations was found. Thus, one can assume that the speech signals' attenuation or amplification caused by the different room acoustics of the tested vehicles play a more important role for a sufficient speech quality than the varied speech timbre or other parameters. Only at very high speech-to-noise ratios ( ≥ 20 dB with A-weighting), room-acoustical parameters such as IACC or the reverberation time are more determining for the speech quality appreciation than the speech's sound pressure level.

Marketing China to U.S. travelers through electronic word-of-mouth and destination image: Taking Beijing as an example

2021 ◽  
pp. 135676672098786
Author(s):  
Li Ran ◽  
Luo Zhenpeng ◽  
Anil Bilgihan ◽  
Fevzi Okumus
Keyword(s):  
Quantitative Research ◽  
Behavioral Control ◽  
Strong Relationship ◽  
Tourism Industry ◽  
Destination Image ◽  
Perceived Behavioral Control ◽  
Online Data ◽  
Mediating Role ◽  
Study Results ◽  
The Impact

The tourism industry in China has grown significantly over the last two decades. Most of the growth, however, is fueled by domestic tourism. As one of the biggest tourism markets in the world, U.S. tourists might be reluctant to travel to China due to reasons such as unfamiliarity, cultural differences, visa requirements, and long flights. Building on the Theory of Planned Behavior (TPB) with relevant constructs, this research proposes that building a strong destination image via eWOM may influence the attitude and intention of U.S. travelers to visit Beijing. More specifically, the current research aims to examine the impact of eWOM and destination image on travel intention of tourists. This study used a quantitative research method and online data collection was conducted through Qualtrics. A total of 413 valid responses from U.S. residents were collected. The statistical software SPSS 21.0 and Mplus 7.0 were used to analyze the data. Study results show a strong relationship between eWOM utilitarian function and eWOM credibility, and eWOM credibility has a significant influence on destination image. Although there was no direct impact of destination image on tourists’ future travel intention, destination image plays a mediating role between eWOM credibility and perceived behavioral control (and tourists’ attitudes as well). Finally, perceived behavioral control and tourists’ attitudes mediate the impact of destination image on travel intention.

Blind Identification of Impact Forces From Multiple Remote Vibratory Measurements

2000 ◽  
Author(s):  
J. Antunes ◽  
P. Izquierdo ◽  
M. Paulino
Keyword(s):  
Explicit Knowledge ◽  
Transfer Functions ◽  
Dynamical Behavior ◽  
Practical Interest ◽  
Operating Conditions ◽  
Blind Identification ◽  
Minimum Entropy ◽  
Response Measurement ◽  
Identification Techniques ◽  
The Impact

Abstract Structures and mechanical components are often subjected to impulsive forces. There is a need for identification techniques which enable monitoring of such loads under operating conditions. For safety reasons and convenience, force identification must often be based on response motions sensed at accessible locations, remote from the impact points. In our previous work we presented techniques for the experimental identification of both isolated impacts and complex rattling forces on a beam, generated at a single and also at several impacting supports. The system dynamical behavior was modeled using traveling flexural beam waves. Although successful, these techniques obviously assume a good understanding of the system dynamic parameters. This is not always the case, a fact that highlights the practical interest of blind identification techniques. This relatively recent field, connected with higher-order statistics, avoids any explicit knowledge of the system transfer functions or impulse responses. Our previous work, based on a single response measurement, is extended in this paper to include several simultaneous responses. We develop a multi-trace version of Wiggins minimum-entropy blind deconvolution algorithm. From numerical simulations and experiments, it is shown that the robustness to noise contamination is increased by using multiple response data. These results suggest that blind identification techniques will prove very useful in practical situations.

The Impact of Performance Variables on Industry in K-Economy

2013 ◽  
Vol 64 (2) ◽  
Author(s):  
Muhammad Jawad Iqbal ◽  
Ibn-e- Hassan
Keyword(s):  
Knowledge Economy ◽  
Structural Equation ◽  
Strong Relationship ◽  
Equation Modeling ◽  
Survey Questionnaire ◽  
Performance Variables ◽  
Complementary Resources ◽  
The Impact ◽  
Associated Variables

In the knowledge economy, companies are thought to be the experts who develop innovative product or service as per demand and then market it to generate the revenue. The role of industry in a knowledge economy is to search and to promote inter-organizational collaborations for learning and to search linkages to arrange for complementary resources. These interactions improve the performance of industry in the knowledge economy. This research has been conducted to find out the impact of industry associated variables that significantly influence the performance of knowledge economy. Important attributes have therefore, been identified from the studies conducted in the field of knowledge economy. Influence of identified attributes on industry has been measured using structural equation modeling (SEM) technique. Data has been collected using survey questionnaire. Findings of the study confirm that there exist a strong relationship among the industry and it’s identified variables that collectively influence the performance of industry in the knowledge economy.

scholarly journals The Impact Facebook and Twitter has on the Cognitive Social Capital of University Students

10.28945/2160 ◽  
2015 ◽  
Vol 18 ◽  
pp. 001-030 ◽  
Author(s):  
Chad Petersen ◽  
Kevin A. Johnston
Keyword(s):  
Social Capital ◽  
University Students ◽  
Online Social Network ◽  
Strong Relationship ◽  
Western Cape ◽  
Network Systems ◽  
University Life ◽  
Facebook Usage ◽  
Cognitive Social Capital ◽  
The Impact

The impact that Facebook and Twitter usage has on the creation and maintenance of university student’s cognitive social capital was investigated on students in the Western Cape province of South Africa. Facebook and Twitter were selected as part of the research context because both are popular online social network systems (SNSs), and few studies were found that investigated the impact that both Facebook and Twitter have on the cognitive social capital of South African university students. Data was collected from a survey questionnaire, which was successfully completed by over 100 students from all 5 universities within the Western Cape. The questionnaire was obtained from a previous study, allowing comparisons to be made. Analysis of the results however, did not show a strong relationship between the intensity of Facebook and Twitter usage, and the various forms of social capital. Facebook usage was found to correlate with student’s satisfaction with university life; which suggests that increasing the intensity of Facebook usage for students experiencing low satisfaction with university life might be beneficial.

scholarly journals Impact of Training on Employees' Performance

2020 ◽  
Vol V (III) ◽  
pp. 120-128
Author(s):  
Muhammad Zia-ur-Rehman ◽  
Riaz Hussain Ansari ◽  
Himayat Ali
Keyword(s):  
Strong Correlation ◽  
Employee Performance ◽  
Strong Relationship ◽  
Levels Of Analysis ◽  
Future Research ◽  
Hr Practices ◽  
The Impact ◽  
Positive Effect

The aim of this paper is to examine the impact of training on employee performance. The study investigates the association and offers proposals for additional investigations. There is a need to check the HR Practices and also quantify the effect across employees' performance. Based on the studies conducted by previous researchers, the study shows similar results that training practices and employee performance have a strong correlation. Taking other aspects into account, it can be said in general that the research discoveries are shifted; however, context remains similar. A few studies have discovered a positive affiliation, some negative, and some no affiliation at all. It was found from the result of the study that training has a positive effect on employee performance. This study shows that there is a solid relationship between training and employee performance. Therefore our result matched with the above researcher. The research shows a strong relationship between training and employee performance. The paper concludes with direction for future research by putting in the various levels of analysis on investigating the effect of training on employee performance.

scholarly journals Impact of climate change on hydropower production in the Upper Danube

2021 ◽  
Author(s):  
Ignacio Martin Santos ◽  
Mathew Herrnegger ◽  
Hubert Holzmann
Keyword(s):  
Climate Change ◽  
Spatial Resolution ◽  
Transfer Functions ◽  
Danube River ◽  
Emission Scenarios ◽  
Hydropower Generation ◽  
Climate Change Projections ◽  
Impact Of Climate Change ◽  
The Future ◽  
The Impact

<p>In the last two decades, different climate downscaling initiatives provided climate scenarios for Europe. The most recent initiative, CORDEX, provides Regional Climate Model (RCM) data for Europe with a spatial resolution of 12.5 km, while the previous initiative, ENSEMBLES, had a spatial resolution of 25 km. They are based on different emission scenarios, Representative Concentration Pathways (RCPs) and Special Report on Emission Scenarios (SRES) respectively.</p><p>A study carried out by Stanzel et al. (2018) explored the hydrological impact and discharge projections for the Danube basin upstream of Vienna when using either CORDEX and ENSEMBLES data. This basin covers an area of 101.810<sup></sup>km<sup>2</sup> with a mean annual discharge of 1923 m<sup>3</sup>/s at the basin outlet. The basin is dominated by the Alps, large gradients and is characterized by high annual precipitations sums which provides valuable water resources available along the basin. Hydropower therefore plays an important role and accounts for more than half of the installed power generating capacity for this area. The estimation of hydropower generation under climate change is an important task for planning the future electricity supply, also considering the on-going EU efforts and the “Green Deal” initiative.</p><p>Taking as input the results from Stanzel et al. (2018), we use transfer functions derived from historical discharge and hydropower generation data, to estimate potential changes for the future. The impact of climate change projections of ENSEMBLE and CORDEX in respect to hydropower generation for each basin within the study area is determined. In addition, an assessment of the impact on basins dominated by runoff river plants versus basins dominated by storage plants is considered.</p><p>The good correlation between discharge and hydropower generation found in the historical data suggests that discharge projection characteristics directly affect the future expected hydropower generation. Large uncertainties exist and stem from the ensembles of climate runs, but also from the potential operation modes of the (storage) hydropower plants in the future.</p><p> </p><p> </p><p>References:</p><p>Stanzel, P., Kling, H., 2018. From ENSEMBLES to CORDEX: Evolving climate change projections for Upper Danube River flow. J. Hydrol. 563, 987–999. https://doi.org/10.1016/j.jhydrol.2018.06.057</p><p> </p>

scholarly journals Comments on "Researcher bias: The use of machine learning in software defect prediction"

2015 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Model ◽  
Strong Association ◽  
Model Performance ◽  
Strong Relationship ◽  
Defect Prediction ◽  
Explanatory Variables ◽  
Software Defect ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

Shepperd et al. (2014) find that the reported performance of a defect prediction model shares a strong relationship with the group of researchers who construct the models. In this paper, we perform an alternative investigation of Shepperd et al. (2014)’s data. We observe that (a) researcher group shares a strong association with the dataset and metric families that are used to build a model; (b) the strong association among the explanatory variables introduces a large amount of interference when interpreting the impact of the researcher group on model performance; and (c) after mitigating the interference, we find that the researcher group has a smaller impact than the metric family. These observations lead us to conclude that the relationship between the researcher group and the performance of a defect prediction model may have more to do with the tendency of researchers to reuse experimental components (e.g., datasets and metrics). We recommend that researchers experiment with a broader selection of datasets and metrics to combat potential bias in their results.

scholarly journals Impact of Background Traffic on Speech Quality in VoWLAN

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Peter Počta ◽  
Peter Kortiš ◽  
Martin Vaculík
Keyword(s):  
Data Transfer ◽  
Speech Quality ◽  
Point Of View ◽  
Telecommunication Networks ◽  
Traffic Load ◽  
Critical Conditions ◽  
Test Sequence ◽  
Perceptual Evaluation ◽  
Background Traffic ◽  
The Impact

This paper describes measurements of the impact of background traffic on speech quality in an environment of WLANs (IEEE 802.11). The simulated background traffic consists of three types of current traffics in telecommunication networks such as data transfer service, multimedia streaming service, and Web service. The background traffic was generated by means of the accomplished Distributed Internet Traffic Generator (D-ITG). The impact of these types of traffic and traffic load on speech quality using the test sequence and speech sequences is the aim of this paper. The assessment of speech quality is carried out by means of the accomplished Perceptual Evaluation of Speech Quality (PESQ) algorithm. The proposal of a new method for improved detection of the critical conditions in wireless telecommunication networks from the speech quality point of view is presented in this paper. Conclusion implies the next application of the method of improved detection of critical conditions for the purpose of algorithms for link adaptation from the speech quality point of view in an environment of WLANs. The primary goal of these algorithms is improving speech quality in the VoWLAN connections, which are established in the competent link.

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