What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Speech recognition
Philipos C. Loizou mostly deals with Speech recognition, Noise, Speech enhancement, Speech processing and Acoustics.
His Speech recognition research is multidisciplinary, relying on both Colors of noise and Noise measurement.
As part of his studies on Noise, Philipos C. Loizou often connects relevant subjects like Subspace topology.
Philipos C. Loizou combines subjects such as Algorithm, Estimator, Noise reduction and Distortion with his study of Speech enhancement.
Philipos C. Loizou has researched Noise reduction in several fields, including Voice activity detection and Speech coding.
Philipos C. Loizou interconnects Consonant, Vowel, Sine wave and Cochlear implant in the investigation of issues within Acoustics.
His most cited work include:
- Speech Enhancement: Theory and Practice (1574 citations)
- Evaluation of Objective Quality Measures for Speech Enhancement (1114 citations)
- Subjective comparison and evaluation of speech enhancement algorithms (491 citations)
What are the main themes of his work throughout his whole career to date?
Speech recognition, Cochlear implant, Intelligibility, Acoustics and Noise are his primary areas of study.
His Speech recognition research integrates issues from Speech enhancement, Noise reduction and Algorithm.
His work in Cochlear implant addresses subjects such as Signal processing, which are connected to disciplines such as Blind signal separation.
The study incorporates disciplines such as Background noise and Reverberation in addition to Intelligibility.
In the subject of general Acoustics, his work in Weighting is often linked to Speech Acoustics, thereby combining diverse domains of study.
His research investigates the connection between Noise and topics such as Subspace topology that intersect with problems in Pattern recognition.
He most often published in these fields:
- Speech recognition (65.82%)
- Cochlear implant (34.69%)
- Intelligibility (30.61%)
What were the highlights of his more recent work (between 2011-2020)?
- Speech recognition (65.82%)
- Intelligibility (30.61%)
- Cochlear implant (34.69%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Speech recognition, Intelligibility, Cochlear implant, Acoustics and Reverberation.
His work deals with themes such as Speech enhancement, Noise reduction and Microphone, which intersect with Speech recognition.
His Speech enhancement study combines topics from a wide range of disciplines, such as Measure, Closed-form expression and Distortion.
His research in Intelligibility intersects with topics in Sentence, Hearing aid and Coherence.
His Cochlear implant study integrates concerns from other disciplines, such as Human–computer interaction, Background noise, Speech processing and Flexibility.
The concepts of his Acoustics study are interwoven with issues in Linear prediction and Communication channel.
Between 2011 and 2020, his most popular works were:
- Development and validation of the AzBio sentence lists. (261 citations)
- A Dual-Microphone Speech Enhancement Algorithm Based on the Coherence Function (81 citations)
- The combined effects of reverberation and noise on speech intelligibility by cochlear implant listeners (53 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Speech recognition
His main research concerns Intelligibility, Speech recognition, Cochlear implant, Reverberation and Acoustics.
His research integrates issues of Sentence and Natural language processing in his study of Intelligibility.
Philipos C. Loizou focuses mostly in the field of Speech recognition, narrowing it down to topics relating to Microphone and, in certain cases, Noise reduction, Wiener filter and Anechoic chamber.
His research combines Speech processing and Cochlear implant.
All of his Acoustics and Noise and Modulation investigations are sub-components of the entire Acoustics study.
His research in Noise intersects with topics in Channel, Entropy and Selection.
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