Pedro Gómez-Vilda

Prof. Pedro Gómez-Vilda

Universidad Politécnica de Madrid
orcid

Research Focus & Metrics

Academic Career Length

45Years (1982-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 October 2020 in Neurocomputing
Verified
Journal Article
Closed access
JGJuan M. Górriz
Juan M. Górriz
JRJavier Ramírez
Javier Ramírez
AOAndrés Ortíz
Andrés Ortíz
FMFrancisco J. Martínez-Murcia
Francisco J. Martínez-Murcia
FSFermin Segovia
Fermin Segovia
Jose FerrandezJose Ferrandez
Jose Ferrandez

18 September 2006 in IEEE Transactions on Biomedical Engineering
Verified
Journal Article
Closed access
JGJuan I. Godino-Llorente
Juan I. Godino-Llorente
PGPedro Gómez-Vilda
Pedro Gómez-Vilda
Manuel Blanco-VelascoManuel Blanco-Velasco
Manuel Blanco-Velasco
Voice diseases have been increasing dramatically in recent times due mainly to unhealthy social habits and voice abuse. These diseases must be diagnosed and treated at an early stage, especially in the case of larynx cancer. It is widely recognized that vocal and voice diseases do not necessarily cause changes in voice quality as perceived by a listener. Acoustic analysis could be a useful tool to diagnose this type of disease. Preliminary research has shown that the detection of voice alterations can be carried out by means of Gaussian mixture models and short-term mel cepstral parameters complemented by frame energy together with first and second derivatives. This paper, using the F-Ratio and Fisher's discriminant ratio, will demonstrate that the detection of voice impairments can be performed using both mel cepstral vectors and their first derivative, ignoring the second derivative

30 January 2004 in IEEE Transactions on Biomedical Engineering
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Journal Article
Closed access
JGJuan I. Godino-Llorente
Juan I. Godino-Llorente
PGPedro Gómez-Vilda
Pedro Gómez-Vilda
It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders-including glottic cancer-under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.

1 November 2015 in Neurocomputing
Verified
Journal Article
Closed access
JMJiri Mekyska
Jiri Mekyska
EJEva Janousova
Eva Janousova
PGPedro Gómez-Vilda
Pedro Gómez-Vilda
ZSZdenek Smekal
Zdenek Smekal
IRIrena Rektorova
Irena Rektorova
KLKarmele Lopez-De-Ipina
Karmele Lopez-De-Ipina

1 April 2006 in Biomedical Signal Processing and Control
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Journal Article
Closed access
NSNicolás Sáenz-Lechón
Nicolás Sáenz-Lechón
JGJuan I. Godino-Llorente
Juan I. Godino-Llorente
VOVíctor Osma-Ruiz
Víctor Osma-Ruiz
PGPedro Gómez-Vilda
Pedro Gómez-Vilda

1 September 2009 in Speech Communication
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Journal Article
Closed access
PGPedro Gómez-Vilda
Pedro Gómez-Vilda
RFRoberto Fernández-Baillo
Roberto Fernández-Baillo
VRVictoria Rodellar-Biarge
Victoria Rodellar-Biarge
VLVíctor Nieto Lluis
Víctor Nieto Lluis
AÁAgustín Álvarez-Marquina
Agustín Álvarez-Marquina
JGJuan Ignacio Godino-Llorente
Juan Ignacio Godino-Llorente