TU Berlin

Quality and Usability LabRafael Zequeira Jiménez

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Rafael Zequeira Jiménez

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Research Topics

  • Speech Quality Assessment in Crowdsourcing  

 

Research Group

Next Generation Crowdsourcing

 

Biography

Rafael Zequeira Jiménez received a degree as Telecommunication Engineer (equivalent to Master of Science) from the University of Granada, Spain in 2014.

From 2013 to 2014 he studied at Technische Universität Berlin within the Erasmus program. At this time, he worked on his Master Thesis entitled: “Secure multi protocol system based on a Resource Model for the IoT and M2M services”. In December 2013, Rafael joined the SNET department of the Deutsche Telekom Innovation Laboratories (T-Labs), where he worked during 10 months as a student research assistant in the TRESOR project. In which he focused on designing and implementing REST APIs to communicate different components.

In June 2015 Rafael joined the Quality and Usability Lab department lead by Prof. Dr.-Ing. Sebastian Möller, to work as Research Assistant in the “Next Generation Crowdsourcing” group, specifically in the Crowdee project. Since 2016, he works towards his PhD in the topic: “Analysis of Crowdsourcing Micro-Tasks for Speech Quality Assessment”.

 

 

Contact

Twitter: @zequeiraj

e-mail:

 

 

Address

Quality and Usability Lab
TU Berlin
Ernst-Reuter-Platz 7
D-10587 Berlin, Germany

Tel:  +4930835358336​

Publications

Evaluating Acoustic Features from Environmental Audio Recordings via Web. A Crowdsourcing Survey on Background Noise Characteristics
Zitatschlüssel zequeirajimenez2019d
Autor Zequeira Jiménez, Rafael and Naderi, Babak and Möller, Sebastian
Buchtitel 45. Deutsche Jahrestagung für Akustik (DAGA 2019)
Seiten 1190–1193
Jahr 2019
ISBN 978-3-939296-14-0
Monat mar
Verlag Deutsche Gesellschaft für Akustik DEGA e.V.
Wie herausgegeben Fullpaper
Zusammenfassung Crowdsourcing permits to reach a large pool of users for gathering and annotating data in an efficient and cost effective manner. In a crowdsourcing context, users employ their own hardware to execute the tasks from the comfort of their environment. However, there is too little information about the users' background noise and environment characteristics, which is mandatory to judge the validity of the data being collected. Specially, in speech quality assessment and audio related tasks. There have been some attempts to investigate the conditions in which users from English speaking countries, India and Asia conduct crowdsourcing tasks, yet any effort have been made regarding the German crowd-workers. To address this issue, a study in a German-based crowdsourcing platform has been conducted. Users answered questions regarding the environment in which they normally execute crowdsourcing tasks. Additionally, while conducting the survey, audio and visual data was collected to validate the submitted answers. This paper reports about the environment conditions in which crowdsourcing tasks are being executed. And we evaluate whether the wavelet time scattering and MFCC features derived from the collected background noise files, are any good to predict the workers environment background noise.
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