World's Best Scientists 2026 revealed!
Juan Ignacio Godino-Llorente

Juan Ignacio Godino-Llorente

D-Index & Metrics

Computer Science

D-Index
37
Citations
5048
World Ranking
10869
National Ranking
174

Overview

Juan Ignacio Godino-Llorente is affiliated with the Technical University of Madrid in Spain. Their research spans multiple areas primarily focused on medicine and computer science, with a significant emphasis on artificial intelligence applications in healthcare.

The main fields of study in their work are:

  • Medicine
  • Computer Science

Their research also covers several subfields, including:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Physiology
  • Biomedical Engineering
  • Experimental and Cognitive Psychology

Juan Ignacio Godino-Llorente's major topics of research include:

  • Voice and Speech Disorders
  • Speech Recognition and Synthesis
  • COVID-19 diagnosis using AI
  • Radiomics and Machine Learning in Medical Imaging
  • Phonetics and Phonology Research
  • Dysphagia Assessment and Management
  • Artificial Intelligence in Healthcare and Education

Their recent papers illustrate a range of contributions to biomedical engineering, signal processing, and artificial intelligence. Selected publications are:

  • "Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects," 2021, published in Biomedical Signal Processing and Control
  • "Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach," 2020, published in PubMed Central
  • "Cardiopulmonary Activity Monitoring Using Millimeter Wave Radars," 2020, published in Remote Sensing
  • "Laryngeal Image Processing of Vocal Folds Motion," 2020, published in Applied Sciences
  • "On the design of automatic voice condition analysis systems. Part III: review of acoustic modelling strategies," 2021, published in Biomedical Signal Processing and Control

Frequent collaborators in their research include:

  • Julián D. Arias-Londoño
  • Jorge A. Gómez-García
  • Laureano Moro-Velázquez
  • Alejandro Guerrero-López
  • Francisco Grandas-Pérez

Their work appears consistently in several notable publication venues such as:

  • arXiv (Cornell University)
  • Biomedical Signal Processing and Control
  • IEEE Access
  • Scientific Data
  • Data in Brief

Best Publications

  • Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters

    J.I. Godino-Llorente;P. Gomez-Vilda;M. Blanco-Velasco

  • Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors

    J.I. Godino-Llorente;P. Gomez-Vilda

  • Methodological issues in the development of automatic systems for voice pathology detection

    Nicolás Sáenz-Lechón;Juan I. Godino-Llorente;Víctor Osma-Ruiz;Pedro Gómez-Vilda

  • Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics

    P. Henriquez;J.B. Alonso;M.A. Ferrer;C.M. Travieso

  • Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and Mel-Cepstral Coefficients

    J D Arias-Londoño;J I Godino-Llorente;N Sáenz-Lechón;V Osma-Ruiz

  • Glottal Source biometrical signature for voice pathology detection

    Pedro Gómez-Vilda;Roberto Fernández-Baillo;Victoria Rodellar-Biarge;Víctor Nieto Lluis

  • Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects

    Laureano Moro-Velazquez;Jorge A. Gomez-Garcia;Julian D. Arias-Londoño;Najim Dehak

  • Cepstral peak prominence: A comprehensive analysis

    Rubén Fraile;Juan Ignacio Godino-Llorente

  • An improved watershed algorithm based on efficient computation of shortest paths

    Víctor Osma-Ruiz;Juan I. Godino-Llorente;Nicolás Sáenz-Lechón;Pedro Gómez-Vilda

  • Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

    A. F. Quiceno-Manrique;J. I. Godino-Llorente;M. Blanco-Velasco;G. Castellanos-Dominguez

  • Digital auscultation analysis for heart murmur detection.

    Edilson Delgado-Trejos;A.F. Quiceno-Manrique;J.I. Godino-Llorente;M. Blanco-Velasco

  • The effectiveness of the glottal to noise excitation ratio for the screening of voice disorders.

    Juan Ignacio Godino-Llorente;Víctor Osma-Ruiz;Nicolás Sáenz-Lechón;Pedro Gómez-Vilda

  • ECG compression with retrieved quality guaranteed

    M. Blanco-Velasco;F. Cruz-Roldan;J.I. Godino-Llorente;K.E. Barner

  • On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices

    Julián David Arias-Londoño;Juan I Godino-Llorente;Maria Markaki;Yannis Stylianou

  • Acoustic analysis of voice using WPCVox: a comparative study with Multi Dimensional Voice Program.

    Juan Ignacio Godino-Llorente;Víctor Osma-Ruiz;Nicolás Sáenz-Lechón;Ignacio Cobeta-Marco

  • Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson's Disease

    Laureano Moro-Velázquez;Jorge Andrés Gómez-García;Juan Ignacio Godino-Llorente;Jesús Villalba

  • Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach

    Julian D. Arias-Londono;Jorge A. Gomez-Garcia;Laureano Moro-Velazquez;Juan I. Godino-Llorente

  • An improved method for voice pathology detection by means of a HMM-based feature space transformation

    Julián D. Arias-Londoño;Juan I. Godino-Llorente;Nicolás Sáenz-Lechón;Víctor Osma-Ruiz

  • Feature Extraction From Parametric Time–Frequency Representations for Heart Murmur Detection

    L. D. Avendaño-Valencia;J. I. Godino-Llorente;M. Blanco-Velasco;G. Castellanos-Dominguez

  • An integrated tool for the diagnosis of voice disorders

    Juan I. Godino-Llorente;Nicolás Sáenz-Lechón;Víctor Osma-Ruiz;Santiago Aguilera-Navarro

  • Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and

    Mel-Cepstral Coefficients;Juan I. Godino-Llorente

Frequent Co-Authors

Najim Dehak
Najim Dehak Johns Hopkins University
Kenneth E. Barner
Kenneth E. Barner University of Delaware
Yannis Stylianou
Yannis Stylianou University of Crete
Ignacio Santamaria
Ignacio Santamaria University of Cantabria
Miguel Ferrer
Miguel Ferrer University of Las Palmas de Gran Canaria
Elmar Nöth
Elmar Nöth University of Erlangen-Nuremberg

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