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TU Berlin

Inhalt des Dokuments

Dr. -Ing. Babak Naderi


Research Interests:

  • Subjective quality assessment
  • Speech Quality Assessment in Crowdsourcing
  • Motivation, Workload, and Performance in Crowdsourcing
  • Statistical Modeling, field data and applied statistics
  • Speech Enhancement
  • Text Complexity and Simplification


Babak Naderi has obtain his Dr.-Ing degree (PhD) on the basis of his thesis with a title of Motivation of Workers on Microtask Crowdsourcing Platforms in September 2017. Babak has Master's degree in Geodesy and Geoinformation Science form the Technical University Berlin with a thesis on "Monte Carlo Localization for Pedestrian Indoor Navigation Using a Map Aided Movement Model". He has also a Bachelor's degree in Software Engineering.

Since August 2012, Babak Naderi is working as a research scientist at the Quality and Usability Lab of  TU-Berlin.

2013-2015 Babak was awarded with an BMBF funded Education program for future IT and Development Leadership involving Bosch, Datev, Deutsche Telekom AG, Holtzbrinck, SAP, Scheer Group, Siemens, and Software AG  amongst highly ranked academic institution (Softwarecampus). He was taking part by leading CrowdMAQA project.

Within dissertation, Babak studies the motivation of crowdworkers in details. He has developed the Crowdwork Motivation Scale for measuring general motivation based on the Self-Determination Theory of Motivation. The scale has been validated within several studies. In addition, he has studied factors influencing the motivation, and influence of different motivation type on the quality of outcomes. Models for predicting task selection strategy of workers are developed, including models for automatically predicting expected workload associated to a task from its design, task acceptance and performance. 

Beside others research activities, Babak is actively working on the standardization of methods for speech quality assessment in crowdsourcing environment in the P.CROWD work program of Study Group 12 in ITU-T Standardization Sector.

Reviewed for WWW, CHI, ICASSP, CSCW, MMSys, PQS, HCOMP, ICWE, QoMEX, International Journal of Human-Computer Studies, Computer Networks, Behaviour & Information Technology, Quality and User Experience.


Selected talks:

  • "Motivation of Crowd Workers, does it matter?",Schloss Dagstuhl, Evaluation in the Crowd: Crowdsourcing and Human-Centred Experiments, November 2015.
  • "Motivation and Quality Assessment in Online Paid Crowdsourcing Micro-task Platforms",Schloss Dagstuhl, Crowdsourcing: From Theory to Practice and Long-Term Perspectives, September 2013.


Office Hours: On Appointment



Quality and Usability Lab

Technische Universität Berlin
Ernst-Reuter-Platz 7
D-10587 Berlin

Tel.:+49 (30) 8353-54221
Fax: +49 (30) 8353-58409



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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