Naomi Harte

Signal Processing · 2012–2018

A researcher in the Science corpus: 10 papers; trusts 15 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 17 citations into it; #1,135 of 7,572 by trust here, #1,411 by citations
• Artificial Intelligence — 12 citations into it; #27,085 of 35,345 by trust here, #31,361 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #16,250 of 21,575 by trust here, #14,822 by citations
• Cognitive Neuroscience — 9 citations into it; #1,966 of 10,852 by trust here, #5,528 by citations
• Evaluation & Benchmarks — 9 citations into it; #2,439 of 3,147 by trust here, #2,545 by citations
• Speech & Audio — 6 citations into it; #689 of 1,076 by trust here, #667 by citations
• Computational Mechanics — 5 citations into it; #4,219 of 10,001 by trust here, #2,644 by citations

Whom Naomi Harte trusts overall

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

1. Andrew Hines
2. Zhou Wang
3. P. Loizou
4. Y. Hu
5. A. Bovik
6. Jan Skoglund
7. Eoin Gillen
8. Hamid R. Sheikh
9. Florian Eyben
10. Anil C. Kokaram

Who trusts Naomi Harte overall

1. Andrew Hines
2. Colm Sloan
3. Jan Skoglund
4. Michael Chinen
5. Anil C. Kokaram
6. Eoin Gillen
7. Miroslaw Narbutt
8. Tong Mo
9. Damien Kelly
10. Felicia S. C. Lim

Papers from Naomi Harte's perspective

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

1. LIBSVM: A library for support vector machines
2. PEAQ - The ITU Standard for Objective Measurement of Perceived Audio Quality
3. Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs
4. Image quality assessment: from error visibility to structural similarity
5. Speech Enhancement: Theory and Practice
6. Evaluation of Objective Quality Measures for Speech Enhancement
7. ViSQOL: an objective speech quality model
8. ViSQOLAudio: An objective audio quality metric for low bitrate codecs.
9. Subjective comparison and evaluation of speech enhancement algorithms (Speech Communication)
10. PEMO-Q—A New Method for Objective Audio Quality Assessment Using a Model of Auditory Perception

Their papers

• Investigating Speech Features for Continuous Turn-Taking Prediction Using LSTMs (2018) — 44 citations
• Objective Assessment of Perceptual Audio Quality Using ViSQOLAudio (2017) — 44 citations
• Bitrate classification of twice-encoded audio using objective quality features (2016) — 3 citations
• ViSQOL: an objective speech quality model (2015) — 202 citations
• ViSQOLAudio: An objective audio quality metric for low bitrate codecs. (2015) — 55 citations
• Measuring and monitoring speech quality for voice over IP with POLQA, viSQOL and p.563 (2015) — 9 citations
• Perceived Audio Quality for Streaming Stereo Music (2014) — 14 citations
• Robustness of speech quality metrics to background noise and network degradations: Comparing ViSQOL, PESQ and POLQA (2013) — 42 citations
• Speech intelligibility prediction using a Neurogram Similarity Index Measure (2012) — 103 citations
• ViSQOL: The Virtual Speech Quality Objective Listener (2012) — 75 citations