Juan Ignacio Godino-Llorente

Universidad Politécnica de Madrid

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Nicolás Sáenz-Lechón

Universidad Politécnica de Madrid

Víctor Osma-Ruiz

Universidad Politécnica de Madrid

Julián D. Arias-Londoño

Universidad Politécnica de Madrid

Jorge A. Gómez-García

Universidad Politécnica de Madrid

Laureano Moro-Velázquez

Johns Hopkins University

Pedro Gómez‐Vilda

Universidad Politécnica de Madrid

Rubén Fraile

Universidad Politécnica de Madrid

Manuel Blanco–Velasco

Universidad de Alcalá

Fernando Cruz–Roldán

Universidad de Alcalá

G. Castellanos-Domínguez

Universidad Nacional de Colombia

Cooperative Institutions

Universidad Politécnica de Madrid

Universidad de Alcalá

Universidad Nacional de Colombia

Universidad Rey Juan Carlos

Universidad de Antioquia

Google (United States)

Hospital General Universitario Gregorio Marañón

Universidad de Las Palmas de Gran Canaria

Polytechnic University of Puerto Rico

Johns Hopkins University

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An integrated tool for the diagnosis of voice disorders

Medical Engineering & Physics (2005)

Juan Ignacio Godino-Llorente Nicolás Sáenz-Lechón Víctor Osma-Ruiz Santiago Aguilera-Navarro Pedro Gómez‐Vilda

Timbre

Spectrogram

10.1016/j.medengphy.2005.04.014

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Citations (55)

Wavelet Packets Feasibility Study for the Design of an ECG Compressor

IEEE Transactions on Biomedical Engineering (2007)

Manuel Blanco–Velasco Fernando Cruz–Roldán Juan Ignacio Godino-Llorente Kenneth E. Barner

Most of the recent electrocardiogram (ECG) compression approaches developed with the wavelet transform are implemented using the discrete wavelet transform. Conversely, wavelet packets (WP) are not extensively used, although they are an adaptive decomposition for representing signals. In this paper, we present a thresholding-based method to encode ECG signals using WP. The design of the compressor has been carried out according to two main goals: (1) The scheme should be simple to allow real-time implementation; (2) quality, i.e., the reconstructed signal should be as similar as possible to the original signal. The proposed scheme is versatile as far as neither QRS detection nor a priori signal information is required. As such, it can thus be applied to any ECG. Results show that WP perform efficiently and can now be considered as an alternative in ECG compression applications.

Second-generation wavelet transform

Lifting Scheme

SIGNAL (programming language)

Stationary wavelet transform

Signal compression

10.1109/tbme.2006.889176

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Citations (48)

Nearly-perfect reconstruction cosine-modulated filter bank applied to ECG signal coding

European Signal Processing Conference (2004)

Fernando Cruz–Roldán Manuel Blanco–Velasco Juan Ignacio Godino-Llorente

This work considers the design of a electrocardiogram (ECG) signal coder based on nearly perfect reconstruction (N-PR) cosine modulated filter banks. The target of the coder is the quality of the reconstructed signal, which must remain within predetermined accuracy limits. We employ as quality criterion the percentage root-mean-square difference (PRD). The tests have been done for the twelve principal cardiac leads, and the amount of compression is evaluated by means of the mean number of bits per sample (MBPS) and the compression ratio (CR). The results confirm the validity of the proposed system.

Filter bank

Signal reconstruction

SIGNAL (programming language)

Signal compression

Root mean square

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Citations (1)

Objective measurements to evaluate glottal space segmentation from laryngeal images

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2012)

Juana M. Gutiérrez-Arriola Víctor Osma-Ruiz Nicolás Sáenz-Lechón Juan Ignacio Godino-Llorente Rubén Fraile

Objective evaluation of the results of medical image segmentation is a known problem. Applied to the task of automatically detecting the glottal area from laryngeal images, this paper proposes a new objective measurement to evaluate the quality of a segmentation algorithm by comparing with the results given by a human expert. The new figure of merit is called Area Index, and its effectiveness is compared with one of the most used figures of merit found in the literature: the Pratt Index. Results over 110 laryngeal images presented high correlations between both indexes, demonstrating that the proposed measure is comparable to the Pratt Index and it is a good indicator of the segmentation quality.

Figure of Merit

10.1109/embc.2012.6347214

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Citations (2)

Subject-Specific Modeling by Domain Adaptation for the Estimation of Subglottal Pressure from Neck-Surface Acceleration Signals

Emiro Ibarra J. V. T. Arias Juan Ignacio Godino-Llorente Daryush D. Mehta Matías Zañartu

Articulatory Phonetics

10.2139/ssrn.4962180

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A new technique for assessing glottal dynamics in speech and singing by means of optical-flow computation

Interspeech 2022 (2015)

Gustavo Andrade-Miranda Nathalie Henrich Bernardoni Juan Ignacio Godino-Llorente

Dynamics

10.21437/interspeech.2015-491

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Citations (3)

Feature selection in pathological voice classification using dinamyc of component analysis

Milton Sarria-Paja Genaro Daza Santacoloma Juan Ignacio Godino-Llorente César Germán Castellanos Domínguez Nicolás Saenz Lechón

This paper presents a methodology for the reduction of the training space based on the analysis of the variation of the linear components of the acoustic features. The methodology is applied to the automatic detection of voice disorders by means of stochastic dynamic models. The acoustic features used to model the speech are: MFCC, HNR, GNE, NNE and the energy envelopes. The feature extraction is carried out by means of PCA, and classification is done using discrete and continuous HMMs. The results showed a direct relationship between the principal directions (feature weights) and the classification performance. The dynamic feature analysis by means of PCA reduces the dimension of the original feature space while the topological complexity of the dynamic classifier remains unchanged. The experiments were tested with Kay Elemetrics (DB1) and UPM (DB2) databases. Results showed 91% of accuracy with 30% of computational cost reduction for DB1.

Mel-frequency cepstrum

Feature vector

Feature (linguistics)

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Citations (2)

Advanced preprocessing of larynx images to improve the segmentation of glottal area.

MAVEBA (2009)

Víctor Osma-Ruiz Juana M. Gutiérrez-Arriola Juan Ignacio Godino-Llorente Nicolás Sáenz-Lechón Rubén Fraile

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Citations (1)

Modulation Spectra Morphological Parameters: A New Method to Assess Voice Pathologies according to the GRBAS Scale

BioMed Research International (2015)

Laureano Moro-Velázquez Jorge A. Gómez-García Juan Ignacio Godino-Llorente Gustavo Andrade-Miranda

Disordered voices are frequently assessed by speech pathologists using perceptual evaluations. This might lead to problems caused by the subjective nature of the process and due to the influence of external factors which compromise the quality of the assessment. In order to increase the reliability of the evaluations, the design of automatic evaluation systems is desirable. With that in mind, this paper presents an automatic system which assesses the Grade and Roughness level of the speech according to the GRBAS perceptual scale. Two parameterization methods are used: one based on the classic Mel-Frequency Cepstral Coefficients, which has already been used successfully in previous works, and other derived from modulation spectra. For the latter, a new group of parameters has been proposed, named Modulation Spectra Morphological Parameters : MSC, DRB, LMR, MSH, MSW, CIL, PALA, and RALA. In methodology, PCA and LDA are employed to reduce the dimensionality of feature space, and GMM classifiers to evaluate the ability of the proposed features on distinguishing the different levels. Efficiencies of 81.6% and 84.7% are obtained for Grade and Roughness, respectively, using modulation spectra parameters, while MFCCs performed 80.5% and 77.7%. The obtained results suggest the usefulness of the proposed Modulation Spectra Morphological Parameters for automatic evaluation of Grade and Roughness in the speech.

Modulation (music)

Mel-frequency cepstrum

Cepstrum

10.1155/2015/259239

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Citations (28)

ByoVoz Automatic Voice Condition Analysis System for the 2018 FEMH Challenge

2021 IEEE International Conference on Big Data (Big Data) (2018)

Julián D. Arias-Londoño Jorge A. Gómez-García Laureano Moro-Velázquez Juan Ignacio Godino-Llorente

This paper presents the methods and results used by the ByoVoz team for the design of an automatic voice condition analysis system, which was submitted to the 2018 Far East Memorial Hospital voice data challenge. The proposed methodology is based on a cascading scheme that firstly discriminates between pathological and normophonic voices, and then identifies the type of disorder. By using diverse feature selection techniques, a subset of complexity, spectral/cepstral and perturbation characteristics were identified for the proposed tasks. Then, several generative classification methodologies based on Gaussian Mixture Models and Gradient Boosting were employed to provide decisions about the input voices in the binary classification, and using one-vs-one classification systems based on Random Forests for the categorization according to the type of disorder. By using a 4-folds cross-validation approach on the training partition a sensitivity=0.93 and specificity=0.74 was obtained. Similarly, an unweighted average recall of 0.63 and an accuracy of 66% was obtained for the identification task. Using the scoring metric proposed in the challenge the final resulting score is of 0.77. When the testing partition was considered, a sensitivity of 0.92, specificity of 0.54, unweighted average recall of 0.61 and a score of 0.72 were obtained. This was the fifth best result in the competition.

Binary classification

Boosting

Gradient boosting

10.1109/bigdata.2018.8622498

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Citations (9)