View source for Publications:Comparing Throat and Acoustic Microphones for Laryngeal Pathology Detection from Human Voice

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{{PublicationSetupTemplate|Author=Jonas Minelga, Adas Gelzinis, Evaldas Vaiciukynas, Antanas Verikas, Marija Bacauskiene, Evaldas Padervinskis, Virgilijus Uloza
|PID=765891
|Name=Minelga, Jonas (Kaunas University of Technology, Kaunas, Lithuania);Gelzinis, Adas (Kaunas University of Technology, Kaunas, Lithuania);Vaiciukynas, Evaldas (Kaunas University of Technology, Kaunas, Lithuania);Verikas, Antanas [av] [0000-0003-2185-8973] (Högskolan i Halmstad [2804], Akademin för informationsteknologi [16904], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941]) (Kaunas University of Technology, Kaunas, Lithuania);Bacauskiene, Marija (Kaunas University of Technology, Kaunas, Lithuania);Padervinskis, Evaldas (Department of Otolaryngology, Lithuanian University of Health Sciences, Kaunas, Lithuania);Uloza, Virgilijus (Department of Otolaryngology, Lithuanian University of Health Sciences, Kaunas, Lithuania)
|Title=Comparing Throat and Acoustic Microphones for Laryngeal Pathology Detection from Human Voice
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=
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|Volume=
|Issue=
|HostPublication=Electrical and Control Technologies : Proceedings of the 9th International Conference on Electrical and Control Technologies ECT-2014
|Conference=ECT 2014 – The 9th International Conference on Electrical and Control Technologies, Kaunas, Lithuania, 8-9 May, 2014
|StartPage=50
|EndPage=53
|Year=2014
|Edition=
|Pages=
|City=Kaunas
|Publisher=Kaunas University of Technology
|Series=Electrical and Control Technologies
|SeriesISSN=1822-5934
|ISBN=
|Urls=http://www.bpti.eu/uploads/Proceedings%20ECT-2014.pdf
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|NBN=urn:nbn:se:hh:diva-27112
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|Keywords=Throat microphone;Acoustic microphone;Voice pathology;Feature extraction;Classification committees
|Categories=Signalbehandling (20205)
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|Abstract=<p>The aim of this study was to compare acoustic and throat microphones in the voice pathology detection task. Recordings of sustained phonation /a/ were used in the study. Each recording was characterized by a rather large set of diverse features, 1051 features in total. Classification into two classes, namely normal and pathological, was performed using random forest committees. Models trained using data obtained from the throat microphone provided lower classification accuracy. This is probably due to a narrower frequency range of the throat microphone leading to loss of important information.</p>
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|CreatedDate=2014-11-25
|PublicationDate=2014-11-25
|LastUpdated=2015-02-03
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:765891}}
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