H. Rufiner

发表

Multiresolution analysis applied to text-independent phone segmentation

A. Cherniz, M. E. Torres, H. Rufiner, 2007 .

Basis Pursuit applied to Speech Signals

H. L. Rufiner, J. Goddard, H. Rufiner, 2001 .

Multiresolution information measures applied to speech recognition

Diego H. Milone, A. Cherniz, M. E. Torres, 2007 .

Self-organizing map clustering based on continuous multiresolution entropy

M. E. Torres, H. Rufiner, H. Torres, 2006 .

Statistical method for sparse coding of speech including a linear predictive model

M. E. Torres, H. Rufiner, J. Goddard, 2006 .

Introducing complexity measures in nonlinear physiological signals: application to robust speech recognition

Diego H. Milone, L. Gamero, M. E. Torres, 2004 .

Computational method for segmentation and classification of ingestive sounds in sheep

Diego H. Milone, H. Rufiner, E. Laca, 2009 .

IFMBE Proceedings 49-Development of a Library for Sound Classification Using Spiking Neural Network

José Tomás Molas, H. Rufiner, I. Peralta, 2015 .

Discriminative power of acoustic features for jaw movement classification in cattle and sheep

Diego H. Milone, José O. Chelotti, H. Rufiner, 2020, Bioacoustics.

Using segment-based features of jaw movements to recognize foraging activities in grazing cattle

Diego H. Milone, Sebastián R. Vanrell, José O. Chelotti, 2022, Biosystems Engineering.

Feature extraction based on bio-inspired model for robust emotion recognition

Diego H. Milone, E. M. Albornoz, H. Rufiner, 2016, Soft Computing.

Post-training discriminative pruning for RBMs

E. M. Albornoz, John Goddard Close, H. Rufiner, 2017, Soft Computing.

Detection of the Glottal Closure Instants Using Empirical Mode Decomposition

H. Rufiner, S. Prasanna, G. Schlotthauer, 2017, Circuits, Systems, and Signal Processing.

Denoising sound signals in a bioinspired non-negative spectro-temporal domain

Diego H. Milone, César Ernesto Martínez, Hugo Leonardo Rufiner, 2015, Digit. Signal Process..

Coherent averaging estimation autoencoders applied to evoked potentials processing

Leandro Daniel Vignolo, Hugo Leonardo Rufiner, Iván E. Gareis, 2017, Neurocomputing.

Classification of ASR Word Hypotheses using prosodic information and resampling of training data

Ramón López-Cózar, Diego H. Milone, E. M. Albornoz, 2013 .

Feature extraction based on bio-inspired model for robust emotion recognition

Diego H. Milone, Enrique Marcelo Albornoz, E. M. Albornoz, 2017, Soft Comput..

Post-training discriminative pruning for RBMs

Enrique Marcelo Albornoz, E. M. Albornoz, Hugo Leonardo Rufiner, 2017, Soft Computing.

Genetic wavelet packets for speech recognition

Diego H. Milone, Leandro Daniel Vignolo, Hugo Leonardo Rufiner, 2013, Expert Syst. Appl..

Spoken emotion recognition using hierarchical classifiers

Diego H. Milone, Enrique Marcelo Albornoz, E. M. Albornoz, 2011, Comput. Speech Lang..

A full end-to-end deep approach for detecting and classifying jaw movements from acoustic signals in grazing cattle

Sebastián R. Vanrell, José O. Chelotti, L. D. Vignolo, 2023, Eng. Appl. Artif. Intell..

Evolutionary algorithm for speech segmentation

Diego H. Milone, D. H. Milone, Hugo Leonardo Rufiner, 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle

Leonardo L. Giovanini, Diego H. Milone, Sebastián R. Vanrell, 2016, Comput. Electron. Agric..

Dimensionality reduction for visualization of normal and pathological speech data

Hugo Leonardo Rufiner, María Eugenia Torres, John C. Goddard, 2009, Biomed. Signal Process. Control..

3d acceleration for heat detection in dairy cows

Diego H. Milone, Sebastián R. Vanrell, José O. Chelotti, 2014 .

` 1-NORM REGULARIZATION FOR SPARSE REPRESENTATION AND P 300 WAVE DETECTION IN B RAIN-C OMPUTER I NTERFACES

H. Rufiner, Victoria Peterson, 2015 .

Spoken Emotion Recognition Using Deep Learning

Enrique Marcelo Albornoz, Fabiola Martínez Licona, E. M. Albornoz, 2014, CIARP.

Hybrid Speech Enhancement with Wiener filters and Deep LSTM Denoising Autoencoders

Hugo Leonardo Rufiner, John Goddard Close, Leandro E. Di Persia, 2018, 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI).

Complexity-Based Discrepancy Measures Applied to Detection of Apnea-Hypopnea Events

Hugo Leonardo Rufiner, Iván E. Gareis, Leandro E. Di Persia, 2018, Complex..

Discriminative methods based on sparse representations of pulse oximetry signals for sleep apnea-hypopnea detection

Hugo Leonardo Rufiner, Leandro E. Di Persia, Ruben D. Spies, 2017, Biomed. Signal Process. Control..

A robust computational approach for jaw movement detection and classification in grazing cattle using acoustic signals

Sebastián R. Vanrell, José O. Chelotti, H. Rufiner, 2022, Comput. Electron. Agric..

A regularity-based algorithm for identifying grazing and rumination bouts from acoustic signals in grazing cattle

Leonardo L. Giovanini, Diego H. Milone, Sebastián R. Vanrell, 2018, Comput. Electron. Agric..

Embedded system for real-time monitoring of foraging behavior of grazing cattle using acoustic signals

Nestor N. Deniz, Leonardo L. Giovanini, José O. Chelotti, 2017, Comput. Electron. Agric..

Automatic recognition of ingestive sounds of cattle based on hidden Markov models

Diego H. Milone, Emilio A. Laca, Julio Galli, 2012, Computers and Electronics in Agriculture.

A noise-robust acoustic method for recognition of foraging activities of grazing cattle

José O. Chelotti, H. Rufiner, J. Galli, 2023, ArXiv.

An online method for estimating grazing and rumination bouts using acoustic signals in grazing cattle

Leonardo L. Giovanini, Diego H. Milone, Sebastián R. Vanrell, 2020, Comput. Electron. Agric..

Audio recordings dataset of grazing jaw movements in dairy cattle

Diego H. Milone, Emilio A. Laca, Sebastián R. Vanrell, 2020, Data in brief.

A pattern recognition approach for detecting and classifying jaw movements in grazing cattle

Leonardo L. Giovanini, Sebastián R. Vanrell, José O. Chelotti, 2018, Comput. Electron. Agric..

Speech emotion recognition using a deep autoencoder

E. M. Albornoz, Enrique M. Albornoz, Hugo L. Rufiner, 2013 .

Sparse and independent representations of speech signals based on parametric models

Hugo Leonardo Rufiner, Luís F. Rocha, John Goddard Close, 2002, INTERSPEECH.

Most Discriminative Atom Selection for Apnea-hypopnea Events Detection

Hugo Leonardo Rufiner, Ruben D. Spies, R. E. Rolón, 2015 .

Extreme Learning Machine Design for Dealing with Unrepresentative Features pdf

Victoria Peterson, Ruben Spies, Nicol'as Nieto, 2019, Neuroinformatics.

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models

H. Rufiner, J. B. Manresa, C. M. Pais, 2023, Computers in Biology and Medicine.

Prediction of the Impact of the End of year Festivities on the Local Epidemiology of COVID-19 Using Agent-Based Simulation with Hidden Markov Models

H. Rufiner, Silvina Saavedra, Camila Engler, 2022, ICCSA.

Generalized sparse discriminant analysis for event-related potential classification

Victoria Peterson, Hugo Leonardo Rufiner, Ruben D. Spies, 2017, Biomed. Signal Process. Control..

Kullback-Leibler Penalized Sparse Discriminant Analysis for Event-Related Potential Classification pdf

Victoria Peterson, Hugo Leonardo Rufiner, Ruben D. Spies, 2016, ArXiv.

Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review

Rajib Sharma, S. R. Mahadeva Prasanna, Leandro Daniel Vignolo, 2017, Speech Commun..

A multi-class structured dictionary learning method using discriminant atom selection

H. Rufiner, R. Spies, L. D. Di Persia, 2020, Pattern Analysis and Applications.

A multi-class structured dictionary learning method using discriminant atom selection pdf

Hugo Leonardo Rufiner, Leandro E. Di Persia, Ruben D. Spies, 2018, Pattern Anal. Appl..

“Thinking out loud”: an open-access EEG-based BCI dataset for inner speech recognition

Rubén D. Spies, H. Rufiner, Victoria Peterson, 2021, bioRxiv.

Open access database of EEG signals recorded during imagined speech

Iván E. Gareis, Germán A. Pressel Coretto, H. Leonardo Rufiner, 2017, Symposium on Medical Information Processing and Analysis.

Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition

H. Rufiner, J. Kamienkowski, Victoria Peterson, 2022, Scientific Data.

Perceptual evaluation of blind source separation for robust speech recognition

Diego H. Milone, Masuzo Yanagida, Hugo Leonardo Rufiner, 2008, Signal Process..

Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals pdf

Hugo Leonardo Rufiner, Iván E. Gareis, Leandro E. Di Persia, 2020, Biomed. Signal Process. Control..

A noise-robust acoustic method for recognizing foraging activities of grazing cattle

José O. Chelotti, H. Rufiner, J. Galli, 2023, 2304.14824.

Emd of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions

Patrick Flandrin, Hugo Leonardo Rufiner, María Eugenia Torres, 2009, Adv. Data Sci. Adapt. Anal..

Bioinspired sparse spectro-temporal representation of speech for robust classification

Diego H. Milone, César Ernesto Martínez, Hugo Leonardo Rufiner, 2012, Comput. Speech Lang..

Auditory Cortical Representations of Speech Signals for Phoneme Classification

Diego H. Milone, César Ernesto Martínez, Hugo Leonardo Rufiner, 2007, MICAI.

Measuring Complexity of Biomedical Signals

Anne Humeau-Heurtier, Javier Escudero, Hugo Leonardo Rufiner, 2018, Complex..

A multiresolution information measure approach to speech recognition

Diego H. Milone, A. Cherniz, M. E. Torres, 2006 .

Determination of an optimal training strategy for a BCI classification task with LDA

I. E. Gareis, R. C. Acevedo, Y. V. Atum, 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

Deep Learning for Emotional Speech Recognition

Enrique Marcelo Albornoz, Fabiola Martínez Licona, E. M. Albornoz, 2014, MCPR.

SVM Scheme for Speech Emotion Recognition using MFCC Feature

Shashidhar G. Koolagudi, E. M. Albornoz, S. Tamil Selvi, 2013 .

Evolutionary cepstral coefficients

Diego H. Milone, Leandro Daniel Vignolo, Hugo Leonardo Rufiner, 2011, Appl. Soft Comput..

Dynamic speech parameterization for text-independent phone segmentation

Maria E. Torres, Analia S. Cherniz, Hugo L. Rufiner, 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

Multiple Feature Extraction and Hierarchical Classifiers for Emotions Recognition

Diego H. Milone, Enrique Marcelo Albornoz, E. M. Albornoz, 2009, COST 2102 Training School.

Objective quality evaluation in blind source separation for speech recognition in a real room

Diego H. Milone, Masuzo Yanagida, Hugo Leonardo Rufiner, 2007, Signal Process..

Time-Scale Information Measures for Text-Independent Phone Segmentation

A. Cherniz, M. E. Torres, H. Rufiner, 2009 .

Comparison between Temporal and Time–Scale Information Measures applied to Speech Recognition ∗

M. E. Torres, H. Rufiner, E. Torres, 2006 .

Evolutionary Splines for Cepstral Filterbank Optimization in Phoneme Classification

Diego H. Milone, Leandro Daniel Vignolo, Hugo Leonardo Rufiner, 2011, EURASIP J. Adv. Signal Process..

Assessment of Homomorphic Analysis for Human Activity Recognition From Acceleration Signals

Diego H. Milone, Sebastián R. Vanrell, H. Leonardo Rufiner, 2018, IEEE Journal of Biomedical and Health Informatics.

Local Discriminant Wavelet Packet Basis for Signal Classification in Brain Computer Interface

Victoria Peterson, Rubén Acevedo, Hugo Leonardo Rufiner, 2015 .

Online Bengali handwritten numerals recognition using Deep Autoencoders

Vineeth N. Balasubramanian, S. R. M. Prasanna, Arghya Pal, 2016, 2016 Twenty Second National Conference on Communication (NCC).

Multimodal Emotion Recognition Using Deep Networks

Hugo Leonardo Rufiner, C. Fadil, R. Alvarez, 2015 .