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Bioacoustics and Medical Acoustics

A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES

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Authors:
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen
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20 April 2018 - 3:14am
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Amir Hossein Poorjam, SNR estimation

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[1] Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen, "A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3090. Accessed: May. 23, 2018.
@article{3090-18,
url = {http://sigport.org/3090},
author = {Amir Hossein Poorjam; Max A. Little; Jesper Rindom Jensen; Mads Græsbøll Christensen },
publisher = {IEEE SigPort},
title = {A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES},
year = {2018} }
TY - EJOUR
T1 - A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES
AU - Amir Hossein Poorjam; Max A. Little; Jesper Rindom Jensen; Mads Græsbøll Christensen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3090
ER -
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. (2018). A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES. IEEE SigPort. http://sigport.org/3090
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen, 2018. A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES. Available at: http://sigport.org/3090.
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. (2018). "A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES." Web.
1. Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3090

A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES

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Authors:
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen
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20 April 2018 - 3:11am
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Amir Hossein Poorjam

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[1] Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen, "A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3089. Accessed: May. 23, 2018.
@article{3089-18,
url = {http://sigport.org/3089},
author = {Amir Hossein Poorjam; Max A. Little; Jesper Rindom Jensen; Mads Græsbøll Christensen },
publisher = {IEEE SigPort},
title = {A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES},
year = {2018} }
TY - EJOUR
T1 - A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES
AU - Amir Hossein Poorjam; Max A. Little; Jesper Rindom Jensen; Mads Græsbøll Christensen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3089
ER -
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. (2018). A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES. IEEE SigPort. http://sigport.org/3089
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen, 2018. A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES. Available at: http://sigport.org/3089.
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. (2018). "A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES." Web.
1. Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3089

BirdVox-full-night: a dataset and website for avian flight call detection.


This article addresses the automatic detection of vocal, nocturnally migrating birds from a network of acoustic sensors.
Thus far, owing to the lack of annotated continuous recordings, existing methods had been benchmarked in a binary classification setting (presence vs. absence).
Instead, with the aim of comparing them in event detection, we release BirdVox-full-night, a dataset of 62 hours of audio comprising 35402 flight calls of nocturnally migrating birds, as recorded from 6 sensors.

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Authors:
Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello
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17 April 2018 - 3:54pm
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[1] Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello, "BirdVox-full-night: a dataset and website for avian flight call detection.", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2941. Accessed: May. 23, 2018.
@article{2941-18,
url = {http://sigport.org/2941},
author = {Vincent Lostanlen; Justin Salamon; Andrew Farnsworth; Steve Kelling; and Juan Pablo Bello },
publisher = {IEEE SigPort},
title = {BirdVox-full-night: a dataset and website for avian flight call detection.},
year = {2018} }
TY - EJOUR
T1 - BirdVox-full-night: a dataset and website for avian flight call detection.
AU - Vincent Lostanlen; Justin Salamon; Andrew Farnsworth; Steve Kelling; and Juan Pablo Bello
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2941
ER -
Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello. (2018). BirdVox-full-night: a dataset and website for avian flight call detection.. IEEE SigPort. http://sigport.org/2941
Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello, 2018. BirdVox-full-night: a dataset and website for avian flight call detection.. Available at: http://sigport.org/2941.
Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello. (2018). "BirdVox-full-night: a dataset and website for avian flight call detection.." Web.
1. Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello. BirdVox-full-night: a dataset and website for avian flight call detection. [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2941

ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF


This paper proposes a novel approach for robustly detecting
multiply repeating audio events in monitoring recordings.
We consider the practically important case that
the sequence of inter onset intervals between subsequent events
is not constant but differs by some jitter. In such cases
classical approaches based on autocorrelation (ACF) are
of limited use. To overcome this problem we propose to use
ACF together with a variant of dynamic time warping. Combining
both techniques in an iterative algorithm, we obtain a

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Authors:
Kevin Wilkinghoff
Submitted On:
14 April 2018 - 6:10am
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[1] Kevin Wilkinghoff, "ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2815. Accessed: May. 23, 2018.
@article{2815-18,
url = {http://sigport.org/2815},
author = {Kevin Wilkinghoff },
publisher = {IEEE SigPort},
title = {ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF},
year = {2018} }
TY - EJOUR
T1 - ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF
AU - Kevin Wilkinghoff
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2815
ER -
Kevin Wilkinghoff. (2018). ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF. IEEE SigPort. http://sigport.org/2815
Kevin Wilkinghoff, 2018. ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF. Available at: http://sigport.org/2815.
Kevin Wilkinghoff. (2018). "ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF." Web.
1. Kevin Wilkinghoff. ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2815

Verbal Protest Recognition in Children with Autism


Real-time detection of verbal protest (sensory overload-induced crying) in children with autism is a first step towards understanding the precursors of challenging behaviors associated with autism. Detection of verbal protest is useful for both autism researchers interested in exploring just-in-time intervention techniques and researchers interested in audio event detection in routine living environments.In this paper, we examine, adapt, and improve upon two techniques for verbal protest recognition and tailor them for children with autism spectrum disorder (ASD).

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Authors:
Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das
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13 April 2018 - 12:25pm
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[1] Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das, "Verbal Protest Recognition in Children with Autism", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2725. Accessed: May. 23, 2018.
@article{2725-18,
url = {http://sigport.org/2725},
author = {Jonah Casebeer; Hillol Sarker; Murtaza Dhuliawala; Nicholas Fay; Mary Pietrowicz; Amar Das },
publisher = {IEEE SigPort},
title = {Verbal Protest Recognition in Children with Autism},
year = {2018} }
TY - EJOUR
T1 - Verbal Protest Recognition in Children with Autism
AU - Jonah Casebeer; Hillol Sarker; Murtaza Dhuliawala; Nicholas Fay; Mary Pietrowicz; Amar Das
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2725
ER -
Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das. (2018). Verbal Protest Recognition in Children with Autism. IEEE SigPort. http://sigport.org/2725
Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das, 2018. Verbal Protest Recognition in Children with Autism. Available at: http://sigport.org/2725.
Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das. (2018). "Verbal Protest Recognition in Children with Autism." Web.
1. Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das. Verbal Protest Recognition in Children with Autism [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2725

AUTOMATIC BIRD VOCALIZATION IDENTIFICATION BASED ON FUSION OF SPECTRAL PATTERN AND TEXTURE FEATURES

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Authors:
Sai-Hua Zhang, Zhao Zhao, Zhi-Yong Xu, Kristen Bellisario, Bryan C. Pijanowski
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12 April 2018 - 10:37pm
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[1] Sai-Hua Zhang, Zhao Zhao, Zhi-Yong Xu, Kristen Bellisario, Bryan C. Pijanowski, "AUTOMATIC BIRD VOCALIZATION IDENTIFICATION BASED ON FUSION OF SPECTRAL PATTERN AND TEXTURE FEATURES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2550. Accessed: May. 23, 2018.
@article{2550-18,
url = {http://sigport.org/2550},
author = {Sai-Hua Zhang; Zhao Zhao; Zhi-Yong Xu; Kristen Bellisario; Bryan C. Pijanowski },
publisher = {IEEE SigPort},
title = {AUTOMATIC BIRD VOCALIZATION IDENTIFICATION BASED ON FUSION OF SPECTRAL PATTERN AND TEXTURE FEATURES},
year = {2018} }
TY - EJOUR
T1 - AUTOMATIC BIRD VOCALIZATION IDENTIFICATION BASED ON FUSION OF SPECTRAL PATTERN AND TEXTURE FEATURES
AU - Sai-Hua Zhang; Zhao Zhao; Zhi-Yong Xu; Kristen Bellisario; Bryan C. Pijanowski
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2550
ER -
Sai-Hua Zhang, Zhao Zhao, Zhi-Yong Xu, Kristen Bellisario, Bryan C. Pijanowski. (2018). AUTOMATIC BIRD VOCALIZATION IDENTIFICATION BASED ON FUSION OF SPECTRAL PATTERN AND TEXTURE FEATURES. IEEE SigPort. http://sigport.org/2550
Sai-Hua Zhang, Zhao Zhao, Zhi-Yong Xu, Kristen Bellisario, Bryan C. Pijanowski, 2018. AUTOMATIC BIRD VOCALIZATION IDENTIFICATION BASED ON FUSION OF SPECTRAL PATTERN AND TEXTURE FEATURES. Available at: http://sigport.org/2550.
Sai-Hua Zhang, Zhao Zhao, Zhi-Yong Xu, Kristen Bellisario, Bryan C. Pijanowski. (2018). "AUTOMATIC BIRD VOCALIZATION IDENTIFICATION BASED ON FUSION OF SPECTRAL PATTERN AND TEXTURE FEATURES." Web.
1. Sai-Hua Zhang, Zhao Zhao, Zhi-Yong Xu, Kristen Bellisario, Bryan C. Pijanowski. AUTOMATIC BIRD VOCALIZATION IDENTIFICATION BASED ON FUSION OF SPECTRAL PATTERN AND TEXTURE FEATURES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2550

Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech


Automatic detection of Parkinson's disease (PD) from speech is a basic step towards computer-aided tools supporting the diagnosis and monitoring of the disease. Although several methods have been proposed, their applicability to real-world situations is still unclear. In particular, the effect of acoustic conditions is not well understood. In this paper, the effects on the accuracy of five different methods to detect PD from speech are evaluated.

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Authors:
Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh
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5 March 2017 - 10:41am
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[1] Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh, "Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1634. Accessed: May. 23, 2018.
@article{1634-17,
url = {http://sigport.org/1634},
author = {Juan Camilo Vásquez-Correa; Joan Serrà; Juan Rafael Orozco-Arroyave; Jesus Francisco Vargas-Bonilla; Elmar Nöh },
publisher = {IEEE SigPort},
title = {Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech},
year = {2017} }
TY - EJOUR
T1 - Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech
AU - Juan Camilo Vásquez-Correa; Joan Serrà; Juan Rafael Orozco-Arroyave; Jesus Francisco Vargas-Bonilla; Elmar Nöh
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1634
ER -
Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh. (2017). Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech. IEEE SigPort. http://sigport.org/1634
Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh, 2017. Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech. Available at: http://sigport.org/1634.
Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh. (2017). "Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech." Web.
1. Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh. Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1634

Online Learning of Time-Frequency Patterns


We present an online method to learn recurring time-frequency patterns from spectrograms. Our method relies on a convolutive decomposition that estimates sequences of spectra into time-frequency patterns and their corresponding activation signals. This method processes one spectrogram at a time such that in comparison with a batch method, the computational cost is reduced proportionally to the number of considered spectrograms. We use a first-order stochastic gradient descent and show that a monotonically decreasing learning-rate works appropriately.

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Authors:
Jose F. Ruiz-Munoz, Raviv Raich, Mauricio Orozco-Alzate, Xiaoli Z. Fern
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1 March 2017 - 3:59pm
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[1] Jose F. Ruiz-Munoz, Raviv Raich, Mauricio Orozco-Alzate, Xiaoli Z. Fern, "Online Learning of Time-Frequency Patterns", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1561. Accessed: May. 23, 2018.
@article{1561-17,
url = {http://sigport.org/1561},
author = {Jose F. Ruiz-Munoz; Raviv Raich; Mauricio Orozco-Alzate; Xiaoli Z. Fern },
publisher = {IEEE SigPort},
title = {Online Learning of Time-Frequency Patterns},
year = {2017} }
TY - EJOUR
T1 - Online Learning of Time-Frequency Patterns
AU - Jose F. Ruiz-Munoz; Raviv Raich; Mauricio Orozco-Alzate; Xiaoli Z. Fern
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1561
ER -
Jose F. Ruiz-Munoz, Raviv Raich, Mauricio Orozco-Alzate, Xiaoli Z. Fern. (2017). Online Learning of Time-Frequency Patterns. IEEE SigPort. http://sigport.org/1561
Jose F. Ruiz-Munoz, Raviv Raich, Mauricio Orozco-Alzate, Xiaoli Z. Fern, 2017. Online Learning of Time-Frequency Patterns. Available at: http://sigport.org/1561.
Jose F. Ruiz-Munoz, Raviv Raich, Mauricio Orozco-Alzate, Xiaoli Z. Fern. (2017). "Online Learning of Time-Frequency Patterns." Web.
1. Jose F. Ruiz-Munoz, Raviv Raich, Mauricio Orozco-Alzate, Xiaoli Z. Fern. Online Learning of Time-Frequency Patterns [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1561

MULTI-VIEW REPRESENTATION LEARNING VIA GCCA FOR MULTIMODAL ANALYSIS OF PARKINSON'S DISEASE


Information from different bio--signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio--signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech--based features. Three different problems are addressed: classification of PD patients vs.

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Authors:
J. C. Vásquez-Correa, J. R. Orozco-Arroyave, R. Arora, E. Nöth, N. Dehak, H. Christensen, F. Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, Phani S. Nidadavolu, M. Yancheva, A. Vann, N. Vogler
Submitted On:
28 February 2017 - 3:28pm
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[1] J. C. Vásquez-Correa, J. R. Orozco-Arroyave, R. Arora, E. Nöth, N. Dehak, H. Christensen, F. Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, Phani S. Nidadavolu, M. Yancheva, A. Vann, N. Vogler, "MULTI-VIEW REPRESENTATION LEARNING VIA GCCA FOR MULTIMODAL ANALYSIS OF PARKINSON'S DISEASE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1515. Accessed: May. 23, 2018.
@article{1515-17,
url = {http://sigport.org/1515},
author = {J. C. Vásquez-Correa; J. R. Orozco-Arroyave; R. Arora; E. Nöth; N. Dehak; H. Christensen; F. Rudzicz; T. Bocklet; M. Cernak; H. Chinaei; J. Hannink; Phani S. Nidadavolu; M. Yancheva; A. Vann; N. Vogler },
publisher = {IEEE SigPort},
title = {MULTI-VIEW REPRESENTATION LEARNING VIA GCCA FOR MULTIMODAL ANALYSIS OF PARKINSON'S DISEASE},
year = {2017} }
TY - EJOUR
T1 - MULTI-VIEW REPRESENTATION LEARNING VIA GCCA FOR MULTIMODAL ANALYSIS OF PARKINSON'S DISEASE
AU - J. C. Vásquez-Correa; J. R. Orozco-Arroyave; R. Arora; E. Nöth; N. Dehak; H. Christensen; F. Rudzicz; T. Bocklet; M. Cernak; H. Chinaei; J. Hannink; Phani S. Nidadavolu; M. Yancheva; A. Vann; N. Vogler
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1515
ER -
J. C. Vásquez-Correa, J. R. Orozco-Arroyave, R. Arora, E. Nöth, N. Dehak, H. Christensen, F. Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, Phani S. Nidadavolu, M. Yancheva, A. Vann, N. Vogler. (2017). MULTI-VIEW REPRESENTATION LEARNING VIA GCCA FOR MULTIMODAL ANALYSIS OF PARKINSON'S DISEASE. IEEE SigPort. http://sigport.org/1515
J. C. Vásquez-Correa, J. R. Orozco-Arroyave, R. Arora, E. Nöth, N. Dehak, H. Christensen, F. Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, Phani S. Nidadavolu, M. Yancheva, A. Vann, N. Vogler, 2017. MULTI-VIEW REPRESENTATION LEARNING VIA GCCA FOR MULTIMODAL ANALYSIS OF PARKINSON'S DISEASE. Available at: http://sigport.org/1515.
J. C. Vásquez-Correa, J. R. Orozco-Arroyave, R. Arora, E. Nöth, N. Dehak, H. Christensen, F. Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, Phani S. Nidadavolu, M. Yancheva, A. Vann, N. Vogler. (2017). "MULTI-VIEW REPRESENTATION LEARNING VIA GCCA FOR MULTIMODAL ANALYSIS OF PARKINSON'S DISEASE." Web.
1. J. C. Vásquez-Correa, J. R. Orozco-Arroyave, R. Arora, E. Nöth, N. Dehak, H. Christensen, F. Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, Phani S. Nidadavolu, M. Yancheva, A. Vann, N. Vogler. MULTI-VIEW REPRESENTATION LEARNING VIA GCCA FOR MULTIMODAL ANALYSIS OF PARKINSON'S DISEASE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1515

Wavelet Features for Classification of VOTE Snore Sounds

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Authors:
Christoph Janott, Zixing Zhang, Clemens Heiser, Bjoern Schuller
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12 March 2016 - 3:45am
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[1] Christoph Janott, Zixing Zhang, Clemens Heiser, Bjoern Schuller, "Wavelet Features for Classification of VOTE Snore Sounds", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/631. Accessed: May. 23, 2018.
@article{631-16,
url = {http://sigport.org/631},
author = {Christoph Janott; Zixing Zhang; Clemens Heiser; Bjoern Schuller },
publisher = {IEEE SigPort},
title = {Wavelet Features for Classification of VOTE Snore Sounds},
year = {2016} }
TY - EJOUR
T1 - Wavelet Features for Classification of VOTE Snore Sounds
AU - Christoph Janott; Zixing Zhang; Clemens Heiser; Bjoern Schuller
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/631
ER -
Christoph Janott, Zixing Zhang, Clemens Heiser, Bjoern Schuller. (2016). Wavelet Features for Classification of VOTE Snore Sounds. IEEE SigPort. http://sigport.org/631
Christoph Janott, Zixing Zhang, Clemens Heiser, Bjoern Schuller, 2016. Wavelet Features for Classification of VOTE Snore Sounds. Available at: http://sigport.org/631.
Christoph Janott, Zixing Zhang, Clemens Heiser, Bjoern Schuller. (2016). "Wavelet Features for Classification of VOTE Snore Sounds." Web.
1. Christoph Janott, Zixing Zhang, Clemens Heiser, Bjoern Schuller. Wavelet Features for Classification of VOTE Snore Sounds [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/631