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Bioacoustics and Medical Acoustics

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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EffectAcousticConditionsParkinsonSpeech.pdf

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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: Dec. 15, 2017.
@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: Dec. 15, 2017.
@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
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28 February 2017 - 3:28pm
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pendonVertical(1).pdf

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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: Dec. 15, 2017.
@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
Submitted On:
12 March 2016 - 3:45am
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kun_poster.pdf

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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: Dec. 15, 2017.
@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