Andrew Hines
Computer Networks and Communications · 2012–2020
A researcher in the Science corpus: 13 papers; trusts 14 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 37 citations into it
- Electrical and Electronic Engineering — 25 citations into it
- Signal Processing — 23 citations into it; #891 of 7,572 by trust here, #1,147 by citations
- Computer Vision and Pattern Recognition — 17 citations into it; #18,232 of 21,575 by trust here, #14,822 by citations
- Speech & Audio — 14 citations into it; #438 of 1,076 by trust here, #561 by citations
- Evaluation & Benchmarks — 12 citations into it; #1,896 of 3,147 by trust here, #2,133 by citations
- Cognitive Neuroscience — 11 citations into it; #1,954 of 10,852 by trust here, #5,291 by citations
- Artificial Intelligence — 10 citations into it; #26,616 of 35,345 by trust here, #24,417 by citations
Whom Andrew Hines trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- P. Loizou
- Naomi Harte
- Zhou Wang
- Y. Hu
- Jan Skoglund
- A. Bovik
- Anil C. Kokaram
- Hamid R. Sheikh
- Eero P. Simoncelli
- Jean-Marc Valin
Who trusts Andrew Hines overall
- Colm Sloan
- Naomi Harte
- Michael Chinen
- Jan Skoglund
- Anil C. Kokaram
- Eoin Gillen
- Damien Kelly
- Felicia S. C. Lim
- Chandan K. A. Reddy
- Tong Mo
Papers from Andrew Hines's perspective
Ordered by how strongly the researchers Andrew Hines trusts endorse each paper. Same corpus, a different order for every researcher.
- Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs
- Speech Enhancement: Theory and Practice
- PEAQ - The ITU Standard for Objective Measurement of Perceived Audio Quality
- Image quality assessment: from error visibility to structural similarity
- LIBSVM: A library for support vector machines
- Evaluation of Objective Quality Measures for Speech Enhancement
- Subjective comparison and evaluation of speech enhancement algorithms (Speech Communication)
- ViSQOL: an objective speech quality model
- ViSQOLAudio: An objective audio quality metric for low bitrate codecs.
- A phenomenological model of the synapse between the inner hair cell and auditory nerve: long-term adaptation with power-law dynamics. (Journal of the Acoustical Society of America)
Their papers
- ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric (2020) — 229 citations
- 5G network slicing using SDN and NFV- A survey of taxonomy, architectures and future challenges (2019) — 678 citations
- Jitter Buffer Compensation in Voice over IP Quality Estimation (2019) — 4 citations
- AMBIQUAL - a full reference objective quality metric for ambisonic spatial audio (2018) — 24 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