Ben Rudzyn

dblp:88/4311 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Speech recognition and synthesis · 50% Robot navigation and mapping · 50%
Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Speech recognition and synthesis › speech separation › computational auditory scene analysis
robot audition
0.112007
Real time robot audition system incorporating both 3D sound source localisation and voice characterisation · ICRA 2007
Robotics › Robot navigation and mapping
sound source localization
0.112007
Real time robot audition system incorporating both 3D sound source localisation and voice characterisation · ICRA 2007
Audio and music processing › speech processing
speech classification
0.012007
Real time robot audition system incorporating both 3D sound source localisation and voice characterisation · ICRA 2007

Methods — techniques the papers use, named apart from their topics

time-delay estimation · 0.1decision tree classifier · 0.1MFCC · 0.1
YearPublicationVenuePosition
2007 Real time robot audition system incorporating both 3D sound source localisation and voice characterisation
abstract
This paper describes the implementation of a novel real time robot audition system which combines a 3D sound localisation system and a voice characterisation (VC) system. The localisation system employs a 4 microphone array and uses the time delay estimation method. Accuracy is improved through the use of a correlation confidence threshold and a median filter. The VC system, which classifies between speech, non speech and silence, uses a decision tree classifier and a feature set comprising MFCCs, mean MFCCs and variance in MFCCs. The complete system has a processing time of 0.73x real time, and a range of up to 3 m. The compact design, high accuracy, and real time processing ability makes the system and the approach well suited to robotics.
Ben Rudzyn, Mohammed Waleed Kadous, Claude Sammut
ICRA1