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Audio and Acoustic Signal Processing

Signboard Saliency Detection in Street Videos

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20 April 2018 - 4:33pm
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[1] , "Signboard Saliency Detection in Street Videos", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3114. Accessed: Jun. 22, 2018.
@article{3114-18,
url = {http://sigport.org/3114},
author = { },
publisher = {IEEE SigPort},
title = {Signboard Saliency Detection in Street Videos},
year = {2018} }
TY - EJOUR
T1 - Signboard Saliency Detection in Street Videos
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3114
ER -
. (2018). Signboard Saliency Detection in Street Videos. IEEE SigPort. http://sigport.org/3114
, 2018. Signboard Saliency Detection in Street Videos. Available at: http://sigport.org/3114.
. (2018). "Signboard Saliency Detection in Street Videos." Web.
1. . Signboard Saliency Detection in Street Videos [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3114

Acoustic Reflector Localization and Classification


The process of understanding acoustic properties of environments is important for several applications, such as spatial audio, augmented reality and source separation. In this paper, multichannel room impulse responses are recorded and transformed into their direction of arrival (DOA)-time domain, by employing a superdirective beamformer. This domain can be represented as a 2D image. Hence, a novel image processing method is proposed to analyze the DOA-time domain, and estimate the reflection times of arrival and DOAs. The main acoustically reflective objects are then localized.

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Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton
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20 April 2018 - 12:07pm
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[1] Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton, "Acoustic Reflector Localization and Classification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3109. Accessed: Jun. 22, 2018.
@article{3109-18,
url = {http://sigport.org/3109},
author = {Luca Remaggi; Hansung Kim; Philip J. B. Jackson; Filippo M. Fazi; Adrian Hilton },
publisher = {IEEE SigPort},
title = {Acoustic Reflector Localization and Classification},
year = {2018} }
TY - EJOUR
T1 - Acoustic Reflector Localization and Classification
AU - Luca Remaggi; Hansung Kim; Philip J. B. Jackson; Filippo M. Fazi; Adrian Hilton
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3109
ER -
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton. (2018). Acoustic Reflector Localization and Classification. IEEE SigPort. http://sigport.org/3109
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton, 2018. Acoustic Reflector Localization and Classification. Available at: http://sigport.org/3109.
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton. (2018). "Acoustic Reflector Localization and Classification." Web.
1. Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton. Acoustic Reflector Localization and Classification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3109

CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS


Coral species, with complex morphology and ambiguous boundaries, pose a great challenge for automated classification. CNN activations, which are extracted from fully connected layers of deep networks (FC features), have been successfully used as powerful universal representations in many visual tasks. In this paper, we investigate the transferability and combined performance of FC features and CONV features (extracted

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Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid
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20 April 2018 - 8:59am
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ICASSP2018 Poster.pdf

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[1] Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid, "CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3103. Accessed: Jun. 22, 2018.
@article{3103-18,
url = {http://sigport.org/3103},
author = {Mohammed Bennamoun; Senjian An; Ferdous Sohel; Farid Boussaid },
publisher = {IEEE SigPort},
title = {CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS},
year = {2018} }
TY - EJOUR
T1 - CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS
AU - Mohammed Bennamoun; Senjian An; Ferdous Sohel; Farid Boussaid
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3103
ER -
Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid. (2018). CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS. IEEE SigPort. http://sigport.org/3103
Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid, 2018. CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS. Available at: http://sigport.org/3103.
Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid. (2018). "CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS." Web.
1. Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid. CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3103

Determined Blind Source Separation via Proximal Splitting Algorithm


The state-of-the-art algorithms of determined blind source separation (BSS) methods based on the independent component analysis

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Authors:
Kohei Yatabe, Daichi Kitamura
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20 April 2018 - 4:23am
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[1] Kohei Yatabe, Daichi Kitamura, "Determined Blind Source Separation via Proximal Splitting Algorithm", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3095. Accessed: Jun. 22, 2018.
@article{3095-18,
url = {http://sigport.org/3095},
author = {Kohei Yatabe; Daichi Kitamura },
publisher = {IEEE SigPort},
title = {Determined Blind Source Separation via Proximal Splitting Algorithm},
year = {2018} }
TY - EJOUR
T1 - Determined Blind Source Separation via Proximal Splitting Algorithm
AU - Kohei Yatabe; Daichi Kitamura
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3095
ER -
Kohei Yatabe, Daichi Kitamura. (2018). Determined Blind Source Separation via Proximal Splitting Algorithm. IEEE SigPort. http://sigport.org/3095
Kohei Yatabe, Daichi Kitamura, 2018. Determined Blind Source Separation via Proximal Splitting Algorithm. Available at: http://sigport.org/3095.
Kohei Yatabe, Daichi Kitamura. (2018). "Determined Blind Source Separation via Proximal Splitting Algorithm." Web.
1. Kohei Yatabe, Daichi Kitamura. Determined Blind Source Separation via Proximal Splitting Algorithm [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3095

Phase Corrected Total Variation for Audio Signals


In optimization-based signal processing, the so-called prior term models the desired signal, and therefore its design is the key factor to achieve a good performance. For audio signals, the time-directional total variation applied to a spectrogram in combination with phase correction has been proposed recently to model sinusoidal components of the signal. Although it is a promising prior, its applicability might be restricted to some extent because of the mismatch of the assumption to the signal.

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Kohei Yatabe, Yasuhiro Oikawa
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20 April 2018 - 4:09am
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2018.04.20_ICASSPポスター_iPC_TV.pdf

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[1] Kohei Yatabe, Yasuhiro Oikawa, "Phase Corrected Total Variation for Audio Signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3093. Accessed: Jun. 22, 2018.
@article{3093-18,
url = {http://sigport.org/3093},
author = {Kohei Yatabe; Yasuhiro Oikawa },
publisher = {IEEE SigPort},
title = {Phase Corrected Total Variation for Audio Signals},
year = {2018} }
TY - EJOUR
T1 - Phase Corrected Total Variation for Audio Signals
AU - Kohei Yatabe; Yasuhiro Oikawa
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3093
ER -
Kohei Yatabe, Yasuhiro Oikawa. (2018). Phase Corrected Total Variation for Audio Signals. IEEE SigPort. http://sigport.org/3093
Kohei Yatabe, Yasuhiro Oikawa, 2018. Phase Corrected Total Variation for Audio Signals. Available at: http://sigport.org/3093.
Kohei Yatabe, Yasuhiro Oikawa. (2018). "Phase Corrected Total Variation for Audio Signals." Web.
1. Kohei Yatabe, Yasuhiro Oikawa. Phase Corrected Total Variation for Audio Signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3093

A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS

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20 April 2018 - 1:51am
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[1] , "A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3083. Accessed: Jun. 22, 2018.
@article{3083-18,
url = {http://sigport.org/3083},
author = { },
publisher = {IEEE SigPort},
title = {A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS},
year = {2018} }
TY - EJOUR
T1 - A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3083
ER -
. (2018). A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS. IEEE SigPort. http://sigport.org/3083
, 2018. A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS. Available at: http://sigport.org/3083.
. (2018). "A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS." Web.
1. . A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3083

On Sequential Random Distortion Testing of Non-Stationary Processes


Random distortion testing (RDT) addresses the problem of testing whether or not a random signal deviates by more than a specified tolerance from a fixed value. The test is non-parametric in the sense that the distribution of the signal under each hypothesis is assumed to be unknown. The signal is observed in independent and identically distributed (i.i.d) additive noise. The need to control the probabilities of false alarm and missed de- tection while reducing the number of samples required to make a decision leads to the SeqRDT approach.

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Dominique Pastor, Vinod Sharma, Pramod K. Varshney
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20 April 2018 - 1:38am
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[1] Dominique Pastor, Vinod Sharma, Pramod K. Varshney, "On Sequential Random Distortion Testing of Non-Stationary Processes", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3078. Accessed: Jun. 22, 2018.
@article{3078-18,
url = {http://sigport.org/3078},
author = {Dominique Pastor; Vinod Sharma; Pramod K. Varshney },
publisher = {IEEE SigPort},
title = {On Sequential Random Distortion Testing of Non-Stationary Processes},
year = {2018} }
TY - EJOUR
T1 - On Sequential Random Distortion Testing of Non-Stationary Processes
AU - Dominique Pastor; Vinod Sharma; Pramod K. Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3078
ER -
Dominique Pastor, Vinod Sharma, Pramod K. Varshney. (2018). On Sequential Random Distortion Testing of Non-Stationary Processes. IEEE SigPort. http://sigport.org/3078
Dominique Pastor, Vinod Sharma, Pramod K. Varshney, 2018. On Sequential Random Distortion Testing of Non-Stationary Processes. Available at: http://sigport.org/3078.
Dominique Pastor, Vinod Sharma, Pramod K. Varshney. (2018). "On Sequential Random Distortion Testing of Non-Stationary Processes." Web.
1. Dominique Pastor, Vinod Sharma, Pramod K. Varshney. On Sequential Random Distortion Testing of Non-Stationary Processes [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3078

AUTOMATIC TEMPORAL SEGMENTATION OF HAND MOVEMENTS FOR HAND POSITIONS RECOGNITION IN FRENCH CUED SPEECH


In the context of Cued Speech (CS) recognition, the recognition
of lips and hand movements is a key task. As we know, a good
temporal segmentation is necessary for the supervised recog-
nition system. However, lips and hand streams cannot share
the same temporal segmentation since they are not synchro-
nized. In this work, we propose a hand preceding model to
predict temporal segmentations of hand movements automati-
cally by exploring the relationship between hand preceding time

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Authors:
LI LIU, GANG FENG, DENIS BEAUTEMPS
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20 April 2018 - 1:08am
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[1] LI LIU, GANG FENG, DENIS BEAUTEMPS, "AUTOMATIC TEMPORAL SEGMENTATION OF HAND MOVEMENTS FOR HAND POSITIONS RECOGNITION IN FRENCH CUED SPEECH", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3072. Accessed: Jun. 22, 2018.
@article{3072-18,
url = {http://sigport.org/3072},
author = {LI LIU; GANG FENG; DENIS BEAUTEMPS },
publisher = {IEEE SigPort},
title = {AUTOMATIC TEMPORAL SEGMENTATION OF HAND MOVEMENTS FOR HAND POSITIONS RECOGNITION IN FRENCH CUED SPEECH},
year = {2018} }
TY - EJOUR
T1 - AUTOMATIC TEMPORAL SEGMENTATION OF HAND MOVEMENTS FOR HAND POSITIONS RECOGNITION IN FRENCH CUED SPEECH
AU - LI LIU; GANG FENG; DENIS BEAUTEMPS
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3072
ER -
LI LIU, GANG FENG, DENIS BEAUTEMPS. (2018). AUTOMATIC TEMPORAL SEGMENTATION OF HAND MOVEMENTS FOR HAND POSITIONS RECOGNITION IN FRENCH CUED SPEECH. IEEE SigPort. http://sigport.org/3072
LI LIU, GANG FENG, DENIS BEAUTEMPS, 2018. AUTOMATIC TEMPORAL SEGMENTATION OF HAND MOVEMENTS FOR HAND POSITIONS RECOGNITION IN FRENCH CUED SPEECH. Available at: http://sigport.org/3072.
LI LIU, GANG FENG, DENIS BEAUTEMPS. (2018). "AUTOMATIC TEMPORAL SEGMENTATION OF HAND MOVEMENTS FOR HAND POSITIONS RECOGNITION IN FRENCH CUED SPEECH." Web.
1. LI LIU, GANG FENG, DENIS BEAUTEMPS. AUTOMATIC TEMPORAL SEGMENTATION OF HAND MOVEMENTS FOR HAND POSITIONS RECOGNITION IN FRENCH CUED SPEECH [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3072

UNSUPERVISED LEARNING APPROACH TO FEATURE ANALYSIS FOR AUTOMATIC SPEECH EMOTION RECOGNITION


The scarcity of emotional speech data is a bottleneck of developing automatic speech emotion recognition (ASER) systems. One way to alleviate this issue is to use unsupervised feature learning techniques to learn features from the widely available general speech and use these features to train emotion classifiers. These unsupervised methods, such as denoising autoencoder (DAE), variational autoencoder (VAE), adversarial autoencoder (AAE) and adversarial variational Bayes (AVB), can capture the intrinsic structure of the data distribution in the learned feature representation.

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Authors:
Sefik Emre Eskimez, Zhiyao Duan, Wendi Heinzelman
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19 April 2018 - 4:01pm
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[1] Sefik Emre Eskimez, Zhiyao Duan, Wendi Heinzelman, "UNSUPERVISED LEARNING APPROACH TO FEATURE ANALYSIS FOR AUTOMATIC SPEECH EMOTION RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3017. Accessed: Jun. 22, 2018.
@article{3017-18,
url = {http://sigport.org/3017},
author = {Sefik Emre Eskimez; Zhiyao Duan; Wendi Heinzelman },
publisher = {IEEE SigPort},
title = {UNSUPERVISED LEARNING APPROACH TO FEATURE ANALYSIS FOR AUTOMATIC SPEECH EMOTION RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - UNSUPERVISED LEARNING APPROACH TO FEATURE ANALYSIS FOR AUTOMATIC SPEECH EMOTION RECOGNITION
AU - Sefik Emre Eskimez; Zhiyao Duan; Wendi Heinzelman
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3017
ER -
Sefik Emre Eskimez, Zhiyao Duan, Wendi Heinzelman. (2018). UNSUPERVISED LEARNING APPROACH TO FEATURE ANALYSIS FOR AUTOMATIC SPEECH EMOTION RECOGNITION. IEEE SigPort. http://sigport.org/3017
Sefik Emre Eskimez, Zhiyao Duan, Wendi Heinzelman, 2018. UNSUPERVISED LEARNING APPROACH TO FEATURE ANALYSIS FOR AUTOMATIC SPEECH EMOTION RECOGNITION. Available at: http://sigport.org/3017.
Sefik Emre Eskimez, Zhiyao Duan, Wendi Heinzelman. (2018). "UNSUPERVISED LEARNING APPROACH TO FEATURE ANALYSIS FOR AUTOMATIC SPEECH EMOTION RECOGNITION." Web.
1. Sefik Emre Eskimez, Zhiyao Duan, Wendi Heinzelman. UNSUPERVISED LEARNING APPROACH TO FEATURE ANALYSIS FOR AUTOMATIC SPEECH EMOTION RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3017

Learning-Based Acoustic Source-Microphone Distance Estimation using the Coherent-to-Diffuse Power Ratio

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19 April 2018 - 3:22pm
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[1] , "Learning-Based Acoustic Source-Microphone Distance Estimation using the Coherent-to-Diffuse Power Ratio", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3010. Accessed: Jun. 22, 2018.
@article{3010-18,
url = {http://sigport.org/3010},
author = { },
publisher = {IEEE SigPort},
title = {Learning-Based Acoustic Source-Microphone Distance Estimation using the Coherent-to-Diffuse Power Ratio},
year = {2018} }
TY - EJOUR
T1 - Learning-Based Acoustic Source-Microphone Distance Estimation using the Coherent-to-Diffuse Power Ratio
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3010
ER -
. (2018). Learning-Based Acoustic Source-Microphone Distance Estimation using the Coherent-to-Diffuse Power Ratio. IEEE SigPort. http://sigport.org/3010
, 2018. Learning-Based Acoustic Source-Microphone Distance Estimation using the Coherent-to-Diffuse Power Ratio. Available at: http://sigport.org/3010.
. (2018). "Learning-Based Acoustic Source-Microphone Distance Estimation using the Coherent-to-Diffuse Power Ratio." Web.
1. . Learning-Based Acoustic Source-Microphone Distance Estimation using the Coherent-to-Diffuse Power Ratio [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3010

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