Babak Naderi

Signal Processing · 2015–2021

A researcher in the Science corpus: 11 papers; trusts 14 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 16 citations into it; #1,021 of 7,572 by trust here, #2,911 by citations
• Artificial Intelligence — 9 citations into it; #26,997 of 35,345 by trust here, #28,481 by citations
• Cognitive Neuroscience — 6 citations into it; #1,802 of 10,852 by trust here, #6,526 by citations
• Computer Vision and Pattern Recognition — 6 citations into it; #14,336 of 21,575 by trust here, #20,756 by citations

Whom Babak Naderi trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Sebastian Möller
2. Y. Hu
3. Tim Polzehl
4. Chandan K. A. Reddy
5. Ross Cutler
6. P. Loizou
7. Friedemann Köster
8. Geoff Cumming
9. Harishchandra Dubey
10. Tobias Hossfeld

Who trusts Babak Naderi overall

1. Ross Cutler
2. Chandan K. A. Reddy
3. Vishak Gopal
4. Hamed Hemati
5. Harishchandra Dubey
6. Gabriel Mittag
7. Jean-Marc Valin
8. Wen-Chin Huang
9. Tianrui Wang
10. Ando Saabas

Papers from Babak Naderi's perspective

Ordered by how strongly the researchers Babak Naderi trusts endorse each paper. Same corpus, a different order for every researcher.

1. Audio Set: An ontology and human-labeled dataset for audio events
2. CROWDMOS: An approach for crowdsourcing mean opinion score studies
3. The Diverse Environments Multi-channel Acoustic Noise Database (DEMAND): A database of multichannel environmental noise recordings
4. Crowdsourcing user studies with Mechanical Turk
5. Quantification of YouTube QoE via Crowdsourcing (IEEE International Symposium on Multimedia)
6. Standards for Internet-based experimenting. (Experimental Psychology)
7. ICASSP 2021 Deep Noise Suppression Challenge
8. Inference by eye: confidence intervals and how to read pictures of data. (American Psychologist)
9. Effect of being observed on the reliability of responses in crowdsourcing micro-task platforms
10. One More Time: How Do You Motivate Employees?

Their papers

• Subjective Evaluation of Noise Suppression Algorithms in Crowdsourcing (2021) — 31 citations
• Effect of trapping questions on the reliability of speech quality judgments in a crowdsourcing paradigm (2015) — 40 citations
• Motivation of Workers on Microtask Crowdsourcing Platforms (2018) — 40 citations
• Robustness in speech quality assessment and temporal training expiry in mobile crowdsourcing environments (2015) — 17 citations
• NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets (2021) — 509 citations
• Transformation of Mean Opinion Scores to Avoid Misleading of Ranked Based Statistical Techniques (2020) — 18 citations
• An Open source Implementation of ITU-T Recommendation P.808 with Validation (2020) — 110 citations
• Impact of the Number of Votes on the Reliability and Validity of Subjective Speech Quality Assessment in the Crowdsourcing Approach (2020) — 13 citations
• Application of Just-Noticeable Difference in Quality as Environment Suitability Test for Crowdsourcing Speech Quality Assessment Task (2020) — 20 citations
• A Crowdsourcing Extension of the ITU-T Recommendation P.835 with Validation (2020) — 11 citations
• Effect of being observed on the reliability of responses in crowdsourcing micro-task platforms (2015) — 21 citations