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Igor Mijic

dblp:211/8680 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0001-7359-686XORCID · reported

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

Artificial intelligence and machine learning · 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%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing
speech production
0.312018
Vocal Analysis of Acoustic Startle Responses · IEEE ACM Trans. Audio Speech Lang. Process. 2018
Audio and music processing › speech analysis
voice analysis
0.312018
Vocal Analysis of Acoustic Startle Responses · IEEE ACM Trans. Audio Speech Lang. Process. 2018

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

surface electromyography · 0.3linear mixed effects model · 0.3
YearPublicationVenuePosition
2018 Vocal Analysis of Acoustic Startle Responses
abstract
This paper presents an extensive statistical analysis of the acoustic startle response of two vocal parameters: fundamental frequency (F0) and root-mean-square energy (E), as well as of the orbicularis oculi (eyeblink) surface electromyography (sEMG). An experiment was conducted in which fourteen participants were exposed to acoustic startle stimuli of varying parameters, i.e., intensity level, duration, rise time, and spectral type, during periods of sustained phonation. Voice recordings of the phonations were taken alongside several physiological signals, of which only the sEMG was analyzed in this paper. Response features (peak value, peak time, latency, rise time, fall time, and duration) were extracted on F0, E and sEMG data, and statistical analysis was conducted using linear mixed effects models to show the response behavior with the varying stimuli. The results for vocal F0 and E data were congruent with sEMG data and earlier work in the field. The results demonstrated that vocal analysis can be used as a feasible alternative to the sEMG eyeblink analysis of acoustic startle responses.
Branimir Dropuljic, Igor Mijic, Davor Petrinovic, Tanja Jovanovic, Kresimir Cosic
IEEE ACM Trans. Audio Speech Lang. Process.2