Dr. -Ing. Babak Naderi
- © Q&U
- 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.
- "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
Tel.:+49 (30) 8353-54221
Fax: +49 (30) 8353-58409
|Author||Naderi, Babak and Möller, Sebastian|
|Title of Book||12th International Conference on Quality of Multimedia Experience (QoMEX)|
|Abstract||Crowdsourcing micro-task platforms facilitate subjective media quality assessment by providing access to a highly scaleable, geographically distributed and demographically diverse pool of crowd workers. Those workers participate in the experiment remotely from their own working environment, using their own hardware. In the case of speech quality assessment, preliminary work showed that environmental noise at the listener’s side and the listening device (loudspeaker or headphone) significantly affect perceived quality, and consequently the reliability and validity of subjective ratings. As a consequence, ITU-T Rec. P.808 specifies requirements for the listening environment of crowd workers when assessing speech quality. In this paper, we propose a new Just Noticeable Difference of Quality (JNDQ) test as a remote screening method for assessing the suitability of the work environment for participating in speech quality assessment tasks. In a laboratory experiment, participants performed this JNDQ test with different listening devices in different listening environments, including a silent room according to ITU-T Rec. P.800 and a simulated background noise scenario. Results show a significant impact of the environment and the listening device on the JNDQ threshold. Thus, the combination of listening device and background noise needs to be screened in a crowdsourcing speech quality test. We propose a minimum threshold of our JNDQ tests an easily applicable screening method for this purpose.|