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Surya Ambrose

dblp:164/8385 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

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.

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

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › SLAM › underwater SLAM
acoustic SLAM
0.212015
On-the-spot calibration of microphone array Transfer Functions for robot audition · ICRA 2015
Robotics › Robot navigation and mapping
SLAM
0.212015
On-the-spot calibration of microphone array Transfer Functions for robot audition · ICRA 2015
Audio and music processing › microphone array processing
microphone array calibration
0.212015
On-the-spot calibration of microphone array Transfer Functions for robot audition · ICRA 2015
Audio and music processing › computational auditory scene analysis
robot audition
0.212015
On-the-spot calibration of microphone array Transfer Functions for robot audition · ICRA 2015
Audio and music processing
sound source localization
0.112015
On-the-spot calibration of microphone array Transfer Functions for robot audition · ICRA 2015

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

transfer function estimation · 0.4interpolation · 0.4
YearPublicationVenuePosition
2015 On-the-spot calibration of microphone array Transfer Functions for robot audition
abstract
This paper investigates the calibration of a microphone array based robot audition system, namely calibration of microphone array Transfer Functions (TFs). There are mainly two methods to obtain TFs: geometrical calculation and measurement. The geometrical calculation has difficulty in simulating robot- and room-acoustics such as diffraction and reflection properties of a robot's body and a room, and the measurement is accurate but time-consuming and requires expertise on acoustics. Thus, we propose fast and simple on-the-spot calibration of TFs including robot- and room-acoustics. The proposed approach first estimates microphone location and clock-difference by Simultaneous Localization And Mapping (SLAM) using hand clap acoustic signals while a human is walking around a robot. Second, TFs with robot- and room-acoustics are estimated by hand clap acoustic signals and interpolated so that the TFs can be roundly arranged at regular intervals in an online manner. In the evaluation, we calibrated TFs only by 20 hand claps (took only 20 seconds), and the TFs showed considerable improvements in sound source localization and separation compared to geometrically calculated TFs and achieved comparable performance towards the measured TFs which are calibrated by approximately 60 minute recordings.
Keisuke Nakamura, Surya Ambrose, Kazuhiro Nakadai
ICRA2