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ICASSP 2018

ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2018 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics. Visit ICASSP 2018.

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning


This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts.

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Authors:
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong
Submitted On:
22 April 2018 - 12:00pm
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Poster for Advantage A2C Dialogue Policy Learning

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[1] Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong, "Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3134. Accessed: Jul. 18, 2018.
@article{3134-18,
url = {http://sigport.org/3134},
author = {Baolin Peng; Xiujun Li; Jianfeng Gao; Jingjing Liu; Yun-Nung Chen; Kam-Fai Wong },
publisher = {IEEE SigPort},
title = {Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning},
year = {2018} }
TY - EJOUR
T1 - Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
AU - Baolin Peng; Xiujun Li; Jianfeng Gao; Jingjing Liu; Yun-Nung Chen; Kam-Fai Wong
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3134
ER -
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. (2018). Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning. IEEE SigPort. http://sigport.org/3134
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong, 2018. Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning. Available at: http://sigport.org/3134.
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. (2018). "Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning." Web.
1. Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3134

AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH


In the partial relaxation approach, at each desired direction, the manifold structure of the remaining interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. By adopting this approach, in this paper, a new estimator based on the unconstrained covariance fitting problem is proposed. To obtain the null-spectra efficiently, an iterative rooting scheme based on the rational function approximation is applied.

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Authors:
Mats Viberg, Marius Pesavento
Submitted On:
22 April 2018 - 11:14am
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slides - ICASSP2018.pdf

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[1] Mats Viberg, Marius Pesavento, "AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3133. Accessed: Jul. 18, 2018.
@article{3133-18,
url = {http://sigport.org/3133},
author = {Mats Viberg; Marius Pesavento },
publisher = {IEEE SigPort},
title = {AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH},
year = {2018} }
TY - EJOUR
T1 - AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH
AU - Mats Viberg; Marius Pesavento
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3133
ER -
Mats Viberg, Marius Pesavento. (2018). AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH. IEEE SigPort. http://sigport.org/3133
Mats Viberg, Marius Pesavento, 2018. AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH. Available at: http://sigport.org/3133.
Mats Viberg, Marius Pesavento. (2018). "AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH." Web.
1. Mats Viberg, Marius Pesavento. AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3133

A 203 FPS VLSI ARCHITECTURE OF IMPROVED DENSE TRAJECTORIES FOR REAL-TIME HUMAN ACTION RECOGNITION


This paper introduces architecture with high throughput, low on-chip memory, and efficient data access for Improved Dense Trajectories (iDT) as video representations for real-time action recognition. The iDT feature can capture long-term motion cues better than any existing deep feature, which makes it crucial in state-of-the-art action recognition systems.

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Authors:
Zhi-Yi Lin, Jia-Lin Chen, Liang-Gee Chen
Submitted On:
22 April 2018 - 10:53am
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ICASSP 2018.pdf

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[1] Zhi-Yi Lin, Jia-Lin Chen, Liang-Gee Chen, "A 203 FPS VLSI ARCHITECTURE OF IMPROVED DENSE TRAJECTORIES FOR REAL-TIME HUMAN ACTION RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3132. Accessed: Jul. 18, 2018.
@article{3132-18,
url = {http://sigport.org/3132},
author = {Zhi-Yi Lin; Jia-Lin Chen; Liang-Gee Chen },
publisher = {IEEE SigPort},
title = {A 203 FPS VLSI ARCHITECTURE OF IMPROVED DENSE TRAJECTORIES FOR REAL-TIME HUMAN ACTION RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - A 203 FPS VLSI ARCHITECTURE OF IMPROVED DENSE TRAJECTORIES FOR REAL-TIME HUMAN ACTION RECOGNITION
AU - Zhi-Yi Lin; Jia-Lin Chen; Liang-Gee Chen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3132
ER -
Zhi-Yi Lin, Jia-Lin Chen, Liang-Gee Chen. (2018). A 203 FPS VLSI ARCHITECTURE OF IMPROVED DENSE TRAJECTORIES FOR REAL-TIME HUMAN ACTION RECOGNITION. IEEE SigPort. http://sigport.org/3132
Zhi-Yi Lin, Jia-Lin Chen, Liang-Gee Chen, 2018. A 203 FPS VLSI ARCHITECTURE OF IMPROVED DENSE TRAJECTORIES FOR REAL-TIME HUMAN ACTION RECOGNITION. Available at: http://sigport.org/3132.
Zhi-Yi Lin, Jia-Lin Chen, Liang-Gee Chen. (2018). "A 203 FPS VLSI ARCHITECTURE OF IMPROVED DENSE TRAJECTORIES FOR REAL-TIME HUMAN ACTION RECOGNITION." Web.
1. Zhi-Yi Lin, Jia-Lin Chen, Liang-Gee Chen. A 203 FPS VLSI ARCHITECTURE OF IMPROVED DENSE TRAJECTORIES FOR REAL-TIME HUMAN ACTION RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3132

A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION


In this paper, we build up a hybrid neural network (NN) for singing melody extraction from polyphonic music by imitating human pitch perception. For human hearing, there are two pitch perception models, the spectral model and the temporal model, in accordance with whether harmonics are resolved or not. Here, we first use NNs to implement individual models and evaluate their performance in the task of singing melody extraction.

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Authors:
Hsin Chou, Ming-Tso Chen, and Tai-Shih Chi
Submitted On:
22 April 2018 - 6:10am
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A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION.pdf

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[1] Hsin Chou, Ming-Tso Chen, and Tai-Shih Chi, "A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3131. Accessed: Jul. 18, 2018.
@article{3131-18,
url = {http://sigport.org/3131},
author = {Hsin Chou; Ming-Tso Chen; and Tai-Shih Chi },
publisher = {IEEE SigPort},
title = {A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION},
year = {2018} }
TY - EJOUR
T1 - A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION
AU - Hsin Chou; Ming-Tso Chen; and Tai-Shih Chi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3131
ER -
Hsin Chou, Ming-Tso Chen, and Tai-Shih Chi. (2018). A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION. IEEE SigPort. http://sigport.org/3131
Hsin Chou, Ming-Tso Chen, and Tai-Shih Chi, 2018. A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION. Available at: http://sigport.org/3131.
Hsin Chou, Ming-Tso Chen, and Tai-Shih Chi. (2018). "A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION." Web.
1. Hsin Chou, Ming-Tso Chen, and Tai-Shih Chi. A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3131

A generative auditory model embedded neural network for speech processing


Before the era of the neural network (NN), features extracted from auditory models have been applied to various speech applications and been demonstrated more robust against noise than conventional speech-processing features. What's the role of auditory models in the current NN era? Are they obsolete? To answer this question, we construct a NN with a generative auditory model embedded to process speech signals.

Paper Details

Authors:
Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi
Submitted On:
22 April 2018 - 5:54am
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A generative auditory model embedded neural network for speech processing.pdf

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[1] Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi, "A generative auditory model embedded neural network for speech processing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3130. Accessed: Jul. 18, 2018.
@article{3130-18,
url = {http://sigport.org/3130},
author = {Yu-Wen Lo; Yih-Liang Shen; Yuan-Fu Liao; and Tai-Shih Chi },
publisher = {IEEE SigPort},
title = {A generative auditory model embedded neural network for speech processing},
year = {2018} }
TY - EJOUR
T1 - A generative auditory model embedded neural network for speech processing
AU - Yu-Wen Lo; Yih-Liang Shen; Yuan-Fu Liao; and Tai-Shih Chi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3130
ER -
Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi. (2018). A generative auditory model embedded neural network for speech processing. IEEE SigPort. http://sigport.org/3130
Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi, 2018. A generative auditory model embedded neural network for speech processing. Available at: http://sigport.org/3130.
Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi. (2018). "A generative auditory model embedded neural network for speech processing." Web.
1. Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi. A generative auditory model embedded neural network for speech processing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3130

A generative auditory model embedded neural network for speech processing


Before the era of the neural network (NN), features extracted from auditory models have been applied to various speech applications and been demonstrated more robust against noise than conventional speech-processing features. What’s the role
of auditory models in the current NN era? Are they obsolete?
To answer this question, we construct a NN with a generative auditory model embedded to process speech signals. The
generative auditory model consists of two stages, the stage of spectrum estimation in the logarithmic-frequency axis by

Paper Details

Authors:
Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi
Submitted On:
22 April 2018 - 5:45am
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generative auditory model, convolutional neural network, multi-resolution, speaker identification

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[1] Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi, "A generative auditory model embedded neural network for speech processing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3129. Accessed: Jul. 18, 2018.
@article{3129-18,
url = {http://sigport.org/3129},
author = {Yu-Wen Lo; Yih-Liang Shen; Yuan-Fu Liao; and Tai-Shih Chi },
publisher = {IEEE SigPort},
title = {A generative auditory model embedded neural network for speech processing},
year = {2018} }
TY - EJOUR
T1 - A generative auditory model embedded neural network for speech processing
AU - Yu-Wen Lo; Yih-Liang Shen; Yuan-Fu Liao; and Tai-Shih Chi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3129
ER -
Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi. (2018). A generative auditory model embedded neural network for speech processing. IEEE SigPort. http://sigport.org/3129
Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi, 2018. A generative auditory model embedded neural network for speech processing. Available at: http://sigport.org/3129.
Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi. (2018). "A generative auditory model embedded neural network for speech processing." Web.
1. Yu-Wen Lo, Yih-Liang Shen, Yuan-Fu Liao, and Tai-Shih Chi. A generative auditory model embedded neural network for speech processing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3129

Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography


Estimation of conduction velocity (CV) is an important task in the analysis of surface electromyography (sEMG). The problem can be framed as estimation of a time-varying delay (TVD) between electrode recordings. In this paper we present an algorithm which incorporates information from multiple electrodes into a single TVD estimation. The algorithm uses a common all-pass filter to relate two groups of signals at a local level.

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Authors:
Adrian Bingham, Thierry Blu, Beth Jelfs
Submitted On:
22 April 2018 - 12:58am
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Presentation Slides

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[1] Adrian Bingham, Thierry Blu, Beth Jelfs, "Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3128. Accessed: Jul. 18, 2018.
@article{3128-18,
url = {http://sigport.org/3128},
author = {Adrian Bingham; Thierry Blu; Beth Jelfs },
publisher = {IEEE SigPort},
title = {Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography},
year = {2018} }
TY - EJOUR
T1 - Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography
AU - Adrian Bingham; Thierry Blu; Beth Jelfs
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3128
ER -
Adrian Bingham, Thierry Blu, Beth Jelfs. (2018). Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography. IEEE SigPort. http://sigport.org/3128
Adrian Bingham, Thierry Blu, Beth Jelfs, 2018. Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography. Available at: http://sigport.org/3128.
Adrian Bingham, Thierry Blu, Beth Jelfs. (2018). "Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography." Web.
1. Adrian Bingham, Thierry Blu, Beth Jelfs. Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3128

On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems

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Authors:
Oguzhan Teke, P. P. Vaidyanathan
Submitted On:
22 April 2018 - 12:28am
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icassp_poster.pdf

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[1] Oguzhan Teke, P. P. Vaidyanathan, "On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3127. Accessed: Jul. 18, 2018.
@article{3127-18,
url = {http://sigport.org/3127},
author = {Oguzhan Teke; P. P. Vaidyanathan },
publisher = {IEEE SigPort},
title = {On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems},
year = {2018} }
TY - EJOUR
T1 - On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems
AU - Oguzhan Teke; P. P. Vaidyanathan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3127
ER -
Oguzhan Teke, P. P. Vaidyanathan. (2018). On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems. IEEE SigPort. http://sigport.org/3127
Oguzhan Teke, P. P. Vaidyanathan, 2018. On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems. Available at: http://sigport.org/3127.
Oguzhan Teke, P. P. Vaidyanathan. (2018). "On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems." Web.
1. Oguzhan Teke, P. P. Vaidyanathan. On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3127

THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE

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Authors:
Oguzhan Teke, P. P. Vaidyanathan
Submitted On:
22 April 2018 - 12:23am
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async_updates_icassp_presentation.pdf

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[1] Oguzhan Teke, P. P. Vaidyanathan, "THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3126. Accessed: Jul. 18, 2018.
@article{3126-18,
url = {http://sigport.org/3126},
author = {Oguzhan Teke; P. P. Vaidyanathan },
publisher = {IEEE SigPort},
title = {THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE},
year = {2018} }
TY - EJOUR
T1 - THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE
AU - Oguzhan Teke; P. P. Vaidyanathan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3126
ER -
Oguzhan Teke, P. P. Vaidyanathan. (2018). THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE. IEEE SigPort. http://sigport.org/3126
Oguzhan Teke, P. P. Vaidyanathan, 2018. THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE. Available at: http://sigport.org/3126.
Oguzhan Teke, P. P. Vaidyanathan. (2018). "THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE." Web.
1. Oguzhan Teke, P. P. Vaidyanathan. THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3126

Shift-Invariant Kernel Additive Modelling for Audio Source Separation


A major goal in blind source separation to identify and separate sources is to model their inherent characteristics. While most state-of- the-art approaches are supervised methods trained on large datasets, interest in non-data-driven approaches such as Kernel Additive Modelling (KAM) remains high due to their interpretability and adaptability. KAM performs the separation of a given source applying robust statistics on the time-frequency bins selected by a source-specific kernel function, commonly the K-NN function.

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Authors:
D. Fano Yela, S. Ewert, K. O'Hanlon, M. Sandler
Submitted On:
21 April 2018 - 10:11pm
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dfy_poster.pdf

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[1] D. Fano Yela, S. Ewert, K. O'Hanlon, M. Sandler, "Shift-Invariant Kernel Additive Modelling for Audio Source Separation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3125. Accessed: Jul. 18, 2018.
@article{3125-18,
url = {http://sigport.org/3125},
author = {D. Fano Yela; S. Ewert; K. O'Hanlon; M. Sandler },
publisher = {IEEE SigPort},
title = {Shift-Invariant Kernel Additive Modelling for Audio Source Separation},
year = {2018} }
TY - EJOUR
T1 - Shift-Invariant Kernel Additive Modelling for Audio Source Separation
AU - D. Fano Yela; S. Ewert; K. O'Hanlon; M. Sandler
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3125
ER -
D. Fano Yela, S. Ewert, K. O'Hanlon, M. Sandler. (2018). Shift-Invariant Kernel Additive Modelling for Audio Source Separation. IEEE SigPort. http://sigport.org/3125
D. Fano Yela, S. Ewert, K. O'Hanlon, M. Sandler, 2018. Shift-Invariant Kernel Additive Modelling for Audio Source Separation. Available at: http://sigport.org/3125.
D. Fano Yela, S. Ewert, K. O'Hanlon, M. Sandler. (2018). "Shift-Invariant Kernel Additive Modelling for Audio Source Separation." Web.
1. D. Fano Yela, S. Ewert, K. O'Hanlon, M. Sandler. Shift-Invariant Kernel Additive Modelling for Audio Source Separation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3125

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