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Audio and Acoustic Signal Processing

Sparse Representation of Human Auditory System


In this paper, three sparse models for the human auditory system are proposed. Biological studies shows that the haircells in the inner ear of the auditory system generate sparse codes from the output of cochlea filterbank. Here, we employ two mathematical sparse representation methods, which are Orthogonal Matching Pursuit (OMP) and K Singular Value Decomposition (K-SVD), in three different strategies for sparse representation of the output of cochlea filterbank that is modeled by a Gammatone filterbank.

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Authors:
Mohammad Edalatian,, Ali Asghar Soltani, Neda Faraji
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4 December 2016 - 1:33pm
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Sparse representation of Human Auditory System

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[1] Mohammad Edalatian,, Ali Asghar Soltani, Neda Faraji , "Sparse Representation of Human Auditory System", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1339. Accessed: Dec. 15, 2017.
@article{1339-16,
url = {http://sigport.org/1339},
author = {Mohammad Edalatian;; Ali Asghar Soltani; Neda Faraji },
publisher = {IEEE SigPort},
title = {Sparse Representation of Human Auditory System},
year = {2016} }
TY - EJOUR
T1 - Sparse Representation of Human Auditory System
AU - Mohammad Edalatian;; Ali Asghar Soltani; Neda Faraji
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1339
ER -
Mohammad Edalatian,, Ali Asghar Soltani, Neda Faraji . (2016). Sparse Representation of Human Auditory System. IEEE SigPort. http://sigport.org/1339
Mohammad Edalatian,, Ali Asghar Soltani, Neda Faraji , 2016. Sparse Representation of Human Auditory System. Available at: http://sigport.org/1339.
Mohammad Edalatian,, Ali Asghar Soltani, Neda Faraji . (2016). "Sparse Representation of Human Auditory System." Web.
1. Mohammad Edalatian,, Ali Asghar Soltani, Neda Faraji . Sparse Representation of Human Auditory System [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1339

GREEDY APPROACHES TO FINDING A SPARSE COVER IN A SENSOR NETWORK WITHOUT LOCATION INFORMATION


Modeling a location-unaware sensor network as a simplicial complex, where simplices correspond to cliques in the communication graph, has proven useful for solving a number of coverage problems under certain conditions using algebraic topology. Several approaches to finding a sparse cover for a fenced sensor network are considered, including calculating homology changes locally, strong collapsing, and Euler characteristic collapsing.

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2 December 2016 - 7:22pm
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Moore-GlobalSIP2016_pdf.pdf

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[1] , "GREEDY APPROACHES TO FINDING A SPARSE COVER IN A SENSOR NETWORK WITHOUT LOCATION INFORMATION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1333. Accessed: Dec. 15, 2017.
@article{1333-16,
url = {http://sigport.org/1333},
author = { },
publisher = {IEEE SigPort},
title = {GREEDY APPROACHES TO FINDING A SPARSE COVER IN A SENSOR NETWORK WITHOUT LOCATION INFORMATION},
year = {2016} }
TY - EJOUR
T1 - GREEDY APPROACHES TO FINDING A SPARSE COVER IN A SENSOR NETWORK WITHOUT LOCATION INFORMATION
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1333
ER -
. (2016). GREEDY APPROACHES TO FINDING A SPARSE COVER IN A SENSOR NETWORK WITHOUT LOCATION INFORMATION. IEEE SigPort. http://sigport.org/1333
, 2016. GREEDY APPROACHES TO FINDING A SPARSE COVER IN A SENSOR NETWORK WITHOUT LOCATION INFORMATION. Available at: http://sigport.org/1333.
. (2016). "GREEDY APPROACHES TO FINDING A SPARSE COVER IN A SENSOR NETWORK WITHOUT LOCATION INFORMATION." Web.
1. . GREEDY APPROACHES TO FINDING A SPARSE COVER IN A SENSOR NETWORK WITHOUT LOCATION INFORMATION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1333

Third dimension for measurement of multi user massive MIMO channels based on LTE advanced downlink


We characterize third-dimension (3D) of channel measurements based on two dimension channel model with fully synchronize

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Authors:
Saeid Aghaeinezhadfirouzja ,Hui Liu ,Bin XIA ,Qun Luo and Weibin Guo
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29 November 2016 - 3:07am
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poster.pdf

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Final GlobalSIP 08-10-2016.pdf

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[1] Saeid Aghaeinezhadfirouzja ,Hui Liu ,Bin XIA ,Qun Luo and Weibin Guo, "Third dimension for measurement of multi user massive MIMO channels based on LTE advanced downlink", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1317. Accessed: Dec. 15, 2017.
@article{1317-16,
url = {http://sigport.org/1317},
author = {Saeid Aghaeinezhadfirouzja ;Hui Liu ;Bin XIA ;Qun Luo and Weibin Guo },
publisher = {IEEE SigPort},
title = {Third dimension for measurement of multi user massive MIMO channels based on LTE advanced downlink},
year = {2016} }
TY - EJOUR
T1 - Third dimension for measurement of multi user massive MIMO channels based on LTE advanced downlink
AU - Saeid Aghaeinezhadfirouzja ;Hui Liu ;Bin XIA ;Qun Luo and Weibin Guo
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1317
ER -
Saeid Aghaeinezhadfirouzja ,Hui Liu ,Bin XIA ,Qun Luo and Weibin Guo. (2016). Third dimension for measurement of multi user massive MIMO channels based on LTE advanced downlink. IEEE SigPort. http://sigport.org/1317
Saeid Aghaeinezhadfirouzja ,Hui Liu ,Bin XIA ,Qun Luo and Weibin Guo, 2016. Third dimension for measurement of multi user massive MIMO channels based on LTE advanced downlink. Available at: http://sigport.org/1317.
Saeid Aghaeinezhadfirouzja ,Hui Liu ,Bin XIA ,Qun Luo and Weibin Guo. (2016). "Third dimension for measurement of multi user massive MIMO channels based on LTE advanced downlink." Web.
1. Saeid Aghaeinezhadfirouzja ,Hui Liu ,Bin XIA ,Qun Luo and Weibin Guo. Third dimension for measurement of multi user massive MIMO channels based on LTE advanced downlink [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1317

Action Classification from Motion Capture Data using Topological Data Analysis


This paper proposes a novel framework for activity recognition from 3D motion capture data using topological data analysis (TDA). We extract point clouds describing the oscillatory patterns of body joints from the principal components of their time series using Taken's delay embedding. Topological persistence from TDA is exploited to extract topological invariants of the constructed point clouds. We propose a feature extraction method from persistence diagrams in order to generate robust low dimensional features used for classification of different activities.

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Authors:
Alireza Dirafzoon, Namita Lokare and Edgar Lobaton
Submitted On:
26 November 2016 - 11:07am
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Poster

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Presentation

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[1] Alireza Dirafzoon, Namita Lokare and Edgar Lobaton, "Action Classification from Motion Capture Data using Topological Data Analysis", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1310. Accessed: Dec. 15, 2017.
@article{1310-16,
url = {http://sigport.org/1310},
author = {Alireza Dirafzoon; Namita Lokare and Edgar Lobaton },
publisher = {IEEE SigPort},
title = {Action Classification from Motion Capture Data using Topological Data Analysis},
year = {2016} }
TY - EJOUR
T1 - Action Classification from Motion Capture Data using Topological Data Analysis
AU - Alireza Dirafzoon; Namita Lokare and Edgar Lobaton
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1310
ER -
Alireza Dirafzoon, Namita Lokare and Edgar Lobaton. (2016). Action Classification from Motion Capture Data using Topological Data Analysis. IEEE SigPort. http://sigport.org/1310
Alireza Dirafzoon, Namita Lokare and Edgar Lobaton, 2016. Action Classification from Motion Capture Data using Topological Data Analysis. Available at: http://sigport.org/1310.
Alireza Dirafzoon, Namita Lokare and Edgar Lobaton. (2016). "Action Classification from Motion Capture Data using Topological Data Analysis." Web.
1. Alireza Dirafzoon, Namita Lokare and Edgar Lobaton. Action Classification from Motion Capture Data using Topological Data Analysis [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1310

Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery


In this paper we propose a hierarchical activity clustering methodology which incorporates the use of topological persistence analysis. Our clustering methodology captures the hierarchies present in the data and is therefore able to show the dependencies that exist between these activities. We make use of an aggregate persistence diagram to select robust graphical structures present within the dataset. These models are stable over a bound and provide accurate classification results.

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Authors:
Namita Lokare, Daniel Benavides, Sahil Juneja, Edgar Lobaton
Submitted On:
26 November 2016 - 11:17am
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Poster

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Presentation

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[1] Namita Lokare, Daniel Benavides, Sahil Juneja, Edgar Lobaton, "Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1309. Accessed: Dec. 15, 2017.
@article{1309-16,
url = {http://sigport.org/1309},
author = {Namita Lokare; Daniel Benavides; Sahil Juneja; Edgar Lobaton },
publisher = {IEEE SigPort},
title = {Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery},
year = {2016} }
TY - EJOUR
T1 - Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery
AU - Namita Lokare; Daniel Benavides; Sahil Juneja; Edgar Lobaton
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1309
ER -
Namita Lokare, Daniel Benavides, Sahil Juneja, Edgar Lobaton. (2016). Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery. IEEE SigPort. http://sigport.org/1309
Namita Lokare, Daniel Benavides, Sahil Juneja, Edgar Lobaton, 2016. Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery. Available at: http://sigport.org/1309.
Namita Lokare, Daniel Benavides, Sahil Juneja, Edgar Lobaton. (2016). "Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery." Web.
1. Namita Lokare, Daniel Benavides, Sahil Juneja, Edgar Lobaton. Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1309

Gotong Royong in NLP Research: A Mobile Tool for Collaborative Text Annotation in Indonesia


The absence of manually annotated training data presents an obstacle for the development of machine-learning based NLP tools in Indonesia. Existing annotation tools lack a mobile-friendly interface which is a problem in Indonesia where most users access the internet using their smartphone. In this paper we propose the first mobile collaborative data annotation tool and evaluate it in an experiment involving 15 Indonesian students who annotated 1500 data records using their smartphones. Users confirmed

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Authors:
Lisa Madlberger, Ayu Purwarianti
Submitted On:
21 November 2016 - 10:49pm
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presentation_IALP2016_46.pdf

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[1] Lisa Madlberger, Ayu Purwarianti, "Gotong Royong in NLP Research: A Mobile Tool for Collaborative Text Annotation in Indonesia ", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1290. Accessed: Dec. 15, 2017.
@article{1290-16,
url = {http://sigport.org/1290},
author = {Lisa Madlberger; Ayu Purwarianti },
publisher = {IEEE SigPort},
title = {Gotong Royong in NLP Research: A Mobile Tool for Collaborative Text Annotation in Indonesia },
year = {2016} }
TY - EJOUR
T1 - Gotong Royong in NLP Research: A Mobile Tool for Collaborative Text Annotation in Indonesia
AU - Lisa Madlberger; Ayu Purwarianti
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1290
ER -
Lisa Madlberger, Ayu Purwarianti. (2016). Gotong Royong in NLP Research: A Mobile Tool for Collaborative Text Annotation in Indonesia . IEEE SigPort. http://sigport.org/1290
Lisa Madlberger, Ayu Purwarianti, 2016. Gotong Royong in NLP Research: A Mobile Tool for Collaborative Text Annotation in Indonesia . Available at: http://sigport.org/1290.
Lisa Madlberger, Ayu Purwarianti. (2016). "Gotong Royong in NLP Research: A Mobile Tool for Collaborative Text Annotation in Indonesia ." Web.
1. Lisa Madlberger, Ayu Purwarianti. Gotong Royong in NLP Research: A Mobile Tool for Collaborative Text Annotation in Indonesia [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1290

A Regression Approach to Valence-Arousal Ratings of Words from Word

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17 November 2016 - 6:54am
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A Regression Approach to Valence-Arousal Ratings of Words from Word-PPT.pdf

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[1] , "A Regression Approach to Valence-Arousal Ratings of Words from Word", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1268. Accessed: Dec. 15, 2017.
@article{1268-16,
url = {http://sigport.org/1268},
author = { },
publisher = {IEEE SigPort},
title = {A Regression Approach to Valence-Arousal Ratings of Words from Word},
year = {2016} }
TY - EJOUR
T1 - A Regression Approach to Valence-Arousal Ratings of Words from Word
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1268
ER -
. (2016). A Regression Approach to Valence-Arousal Ratings of Words from Word. IEEE SigPort. http://sigport.org/1268
, 2016. A Regression Approach to Valence-Arousal Ratings of Words from Word. Available at: http://sigport.org/1268.
. (2016). "A Regression Approach to Valence-Arousal Ratings of Words from Word." Web.
1. . A Regression Approach to Valence-Arousal Ratings of Words from Word [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1268

On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation


Although uni-directional recurrent neural network language
model(RNNLM) has been very successful, it’s hard to train a
bi-directional RNNLM properly due to the generative nature of
language model. In this work, we propose to train bi-directional
RNNLM with noise contrastive estimation(NCE), since the
properities of NCE training will help the model to acheieve
sentence-level normalization. Experiments are conducted on
two hand-crafted tasks on the PTB data set: a rescore task and

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Authors:
Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu
Submitted On:
16 October 2016 - 11:45am
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iscslp2016_poster_v2.pdf

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[1] Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu, "On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1255. Accessed: Dec. 15, 2017.
@article{1255-16,
url = {http://sigport.org/1255},
author = {Tianxing He; Yu Zhang; Jasha Droppo; Kai Yu },
publisher = {IEEE SigPort},
title = {On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation},
year = {2016} }
TY - EJOUR
T1 - On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation
AU - Tianxing He; Yu Zhang; Jasha Droppo; Kai Yu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1255
ER -
Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu. (2016). On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation. IEEE SigPort. http://sigport.org/1255
Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu, 2016. On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation. Available at: http://sigport.org/1255.
Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu. (2016). "On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation." Web.
1. Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu. On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1255

The Preliminary Study of Influence on Tone Perception from Segments

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Authors:
Chong Cao, Yanlu Xie, Ju Lin, Qian Li, Jinsong Zhang
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15 October 2016 - 10:42pm
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the preliminary study of influence on tone perception from segments.pdf

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[1] Chong Cao, Yanlu Xie, Ju Lin, Qian Li, Jinsong Zhang, "The Preliminary Study of Influence on Tone Perception from Segments", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1253. Accessed: Dec. 15, 2017.
@article{1253-16,
url = {http://sigport.org/1253},
author = {Chong Cao; Yanlu Xie; Ju Lin; Qian Li; Jinsong Zhang },
publisher = {IEEE SigPort},
title = {The Preliminary Study of Influence on Tone Perception from Segments},
year = {2016} }
TY - EJOUR
T1 - The Preliminary Study of Influence on Tone Perception from Segments
AU - Chong Cao; Yanlu Xie; Ju Lin; Qian Li; Jinsong Zhang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1253
ER -
Chong Cao, Yanlu Xie, Ju Lin, Qian Li, Jinsong Zhang. (2016). The Preliminary Study of Influence on Tone Perception from Segments. IEEE SigPort. http://sigport.org/1253
Chong Cao, Yanlu Xie, Ju Lin, Qian Li, Jinsong Zhang, 2016. The Preliminary Study of Influence on Tone Perception from Segments. Available at: http://sigport.org/1253.
Chong Cao, Yanlu Xie, Ju Lin, Qian Li, Jinsong Zhang. (2016). "The Preliminary Study of Influence on Tone Perception from Segments." Web.
1. Chong Cao, Yanlu Xie, Ju Lin, Qian Li, Jinsong Zhang. The Preliminary Study of Influence on Tone Perception from Segments [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1253

A Study on perceptual training of Japanese CSL Learner to Discriminate Mandarin Lexical Tones


In process of learning Chinese as a second language (CSL), Japanese natives have difficulties in tone perception. Among the four Chinese lexical tones, the tone pairs Tone 1-Tone 2 and Tone 1-Tone 4 are problematic for Japanese CSL beginners. In order to help them develop efficiently discriminating capability of the tone pairs, we designed a hybrid perceptual training scheme which combined adaptive training and high variability phonetic training.

Paper Details

Authors:
Feiya Li, Yanlu Xie, Xiaomin Yu, Jinsong Zhang
Submitted On:
15 October 2016 - 12:55pm
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ISCSLP-paper177-oral.pdf

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[1] Feiya Li, Yanlu Xie, Xiaomin Yu, Jinsong Zhang, "A Study on perceptual training of Japanese CSL Learner to Discriminate Mandarin Lexical Tones", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1252. Accessed: Dec. 15, 2017.
@article{1252-16,
url = {http://sigport.org/1252},
author = { Feiya Li; Yanlu Xie; Xiaomin Yu; Jinsong Zhang },
publisher = {IEEE SigPort},
title = {A Study on perceptual training of Japanese CSL Learner to Discriminate Mandarin Lexical Tones},
year = {2016} }
TY - EJOUR
T1 - A Study on perceptual training of Japanese CSL Learner to Discriminate Mandarin Lexical Tones
AU - Feiya Li; Yanlu Xie; Xiaomin Yu; Jinsong Zhang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1252
ER -
Feiya Li, Yanlu Xie, Xiaomin Yu, Jinsong Zhang. (2016). A Study on perceptual training of Japanese CSL Learner to Discriminate Mandarin Lexical Tones. IEEE SigPort. http://sigport.org/1252
Feiya Li, Yanlu Xie, Xiaomin Yu, Jinsong Zhang, 2016. A Study on perceptual training of Japanese CSL Learner to Discriminate Mandarin Lexical Tones. Available at: http://sigport.org/1252.
Feiya Li, Yanlu Xie, Xiaomin Yu, Jinsong Zhang. (2016). "A Study on perceptual training of Japanese CSL Learner to Discriminate Mandarin Lexical Tones." Web.
1. Feiya Li, Yanlu Xie, Xiaomin Yu, Jinsong Zhang. A Study on perceptual training of Japanese CSL Learner to Discriminate Mandarin Lexical Tones [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1252

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