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Emerging DSP Applications

Semi-Supervised Optimal Transport Methods for Detecting Anomalies


Building upon advances on optimal transport and anomaly detection, we propose a generalization of an unsupervised and automatic method for detection of significant deviation from reference signals. Unlike most existing approaches for anomaly detection, our method is built on a non-parametric framework exploiting the optimal transportation to estimate deviation from an observed distribution.

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Authors:
Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou
Submitted On:
20 May 2020 - 8:36am
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[1] Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou, "Semi-Supervised Optimal Transport Methods for Detecting Anomalies", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5406. Accessed: Jul. 09, 2020.
@article{5406-20,
url = {http://sigport.org/5406},
author = { Amina Alaoui-Belghiti; Sylvain Chevallier; Eric Monacelli; Guillaume Bao; Eric Azabou },
publisher = {IEEE SigPort},
title = {Semi-Supervised Optimal Transport Methods for Detecting Anomalies},
year = {2020} }
TY - EJOUR
T1 - Semi-Supervised Optimal Transport Methods for Detecting Anomalies
AU - Amina Alaoui-Belghiti; Sylvain Chevallier; Eric Monacelli; Guillaume Bao; Eric Azabou
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5406
ER -
Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou. (2020). Semi-Supervised Optimal Transport Methods for Detecting Anomalies. IEEE SigPort. http://sigport.org/5406
Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou, 2020. Semi-Supervised Optimal Transport Methods for Detecting Anomalies. Available at: http://sigport.org/5406.
Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou. (2020). "Semi-Supervised Optimal Transport Methods for Detecting Anomalies." Web.
1. Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou. Semi-Supervised Optimal Transport Methods for Detecting Anomalies [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5406

FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR


Bridge weigh-in-motion (BWIM) is a technique for detecting heavy vehicles that may cause serious damage to real bridges. BWIM is realized by analyzing the strain signals observed at places on the bridge in terms of bridge-component responses to the axle loads. In current practice, a BWIM system requires multiple strain sensors to collect vehicle properties including speed and axle positions for accurate load estimation, which may limit the system’s life-span.

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Authors:
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi
Submitted On:
27 May 2020 - 11:14am
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icassp20s.pdf

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[1] Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi, "FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5401. Accessed: Jul. 09, 2020.
@article{5401-20,
url = {http://sigport.org/5401},
author = {Takaya Kawakatsu; Kenro Aihara; Atsuhiro Takasu; Jun Adachi },
publisher = {IEEE SigPort},
title = {FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR},
year = {2020} }
TY - EJOUR
T1 - FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR
AU - Takaya Kawakatsu; Kenro Aihara; Atsuhiro Takasu; Jun Adachi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5401
ER -
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi. (2020). FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR. IEEE SigPort. http://sigport.org/5401
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi, 2020. FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR. Available at: http://sigport.org/5401.
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi. (2020). "FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR." Web.
1. Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi. FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5401

Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data


This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors, obtaining labeled training data for each timestamp is not a realistic goal. To deal with this difficulty, we formulate the task as a weakly supervised learning. We only need to add labels for each complete trip, not for every timestamp independently.

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Authors:
Kun Zhao,Takayuki Yoshizumi
Submitted On:
14 May 2020 - 2:52am
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Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data

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[1] Kun Zhao,Takayuki Yoshizumi, "Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5240. Accessed: Jul. 09, 2020.
@article{5240-20,
url = {http://sigport.org/5240},
author = {Kun Zhao;Takayuki Yoshizumi },
publisher = {IEEE SigPort},
title = {Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data},
year = {2020} }
TY - EJOUR
T1 - Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data
AU - Kun Zhao;Takayuki Yoshizumi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5240
ER -
Kun Zhao,Takayuki Yoshizumi. (2020). Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data. IEEE SigPort. http://sigport.org/5240
Kun Zhao,Takayuki Yoshizumi, 2020. Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data. Available at: http://sigport.org/5240.
Kun Zhao,Takayuki Yoshizumi. (2020). "Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data." Web.
1. Kun Zhao,Takayuki Yoshizumi. Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5240

STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS


Smart grids are faced with the challenge of meeting the ever increasing load demands of electric vehicles (EVs). To provide acceptable charging services, operators need to be equipped with an efficient charging stations (CSs) planning strategy. Unfortunately, existing planning solutions are quite limited. They normally rely on standard IEEE bus systems or power grids that are specific to certain cities. In this paper, using stochastic geometry, we formulate the CSs planning on a stochastic geometry-based power grid model, that we previously showed to mimic real-world power grids.

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Authors:
Rachad Atat, Muhammad Ismail, and Erchin Serpedin
Submitted On:
13 May 2020 - 5:04pm
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Presentation_ICASSP_2020.pdf

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[1] Rachad Atat, Muhammad Ismail, and Erchin Serpedin, "STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5144. Accessed: Jul. 09, 2020.
@article{5144-20,
url = {http://sigport.org/5144},
author = {Rachad Atat; Muhammad Ismail; and Erchin Serpedin },
publisher = {IEEE SigPort},
title = {STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS},
year = {2020} }
TY - EJOUR
T1 - STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS
AU - Rachad Atat; Muhammad Ismail; and Erchin Serpedin
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5144
ER -
Rachad Atat, Muhammad Ismail, and Erchin Serpedin. (2020). STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS. IEEE SigPort. http://sigport.org/5144
Rachad Atat, Muhammad Ismail, and Erchin Serpedin, 2020. STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS. Available at: http://sigport.org/5144.
Rachad Atat, Muhammad Ismail, and Erchin Serpedin. (2020). "STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS." Web.
1. Rachad Atat, Muhammad Ismail, and Erchin Serpedin. STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5144

Environment-aware Reconfigurable Noise Suppression


The paper proposes an efficient, robust, and reconfigurable technique to suppress various types of noises for any sampling rate. The theoretical analyses, subjective and objective test results show that the proposed noise suppression (NS) solution significantly enhances the speech transmission index (STI), speech intelligibility (SI), signal-to-noise ratio (SNR), and subjective listening experience. The STI and SI consists of 5 levels, i.e., bad, poor, fair, good, and excellent. The most common noisy condition is of SNR ranging from -5 to 8 dB.

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Authors:
Jun Yang, Joshua Bingham
Submitted On:
20 April 2020 - 4:55am
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Facebook Noise Suppression @ ICASSP 2020

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[1] Jun Yang, Joshua Bingham, "Environment-aware Reconfigurable Noise Suppression", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5110. Accessed: Jul. 09, 2020.
@article{5110-20,
url = {http://sigport.org/5110},
author = {Jun Yang; Joshua Bingham },
publisher = {IEEE SigPort},
title = {Environment-aware Reconfigurable Noise Suppression},
year = {2020} }
TY - EJOUR
T1 - Environment-aware Reconfigurable Noise Suppression
AU - Jun Yang; Joshua Bingham
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5110
ER -
Jun Yang, Joshua Bingham. (2020). Environment-aware Reconfigurable Noise Suppression. IEEE SigPort. http://sigport.org/5110
Jun Yang, Joshua Bingham, 2020. Environment-aware Reconfigurable Noise Suppression. Available at: http://sigport.org/5110.
Jun Yang, Joshua Bingham. (2020). "Environment-aware Reconfigurable Noise Suppression." Web.
1. Jun Yang, Joshua Bingham. Environment-aware Reconfigurable Noise Suppression [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5110

SMART: A SENSOR-TRIGGERRED INTERACTIVE MR DISPLAY


In this paper, a prototype with a hologram display is developed as an interactive MR device, triggered by the sensor values on mobile devices to change the attitude of a virtual object in the virtual world and the real object in a physical world with a synchronizing manner. To provide a consistent displaying content from a physical world to a virtual world in an MR environment, a hologram device with a controlling mobile devices is developed.

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Authors:
Y.S. Lan, C.D. Chen, S.W. Sun, W.C. Yen, Y.T. Wang, Y.H. Yang, J.M. Day, and K.L. Hua
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18 September 2019 - 9:54am
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eposter_ICIP2019_1.pptx

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[1] Y.S. Lan, C.D. Chen, S.W. Sun, W.C. Yen, Y.T. Wang, Y.H. Yang, J.M. Day, and K.L. Hua, "SMART: A SENSOR-TRIGGERRED INTERACTIVE MR DISPLAY", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4678. Accessed: Jul. 09, 2020.
@article{4678-19,
url = {http://sigport.org/4678},
author = {Y.S. Lan; C.D. Chen; S.W. Sun; W.C. Yen; Y.T. Wang; Y.H. Yang; J.M. Day; and K.L. Hua },
publisher = {IEEE SigPort},
title = {SMART: A SENSOR-TRIGGERRED INTERACTIVE MR DISPLAY},
year = {2019} }
TY - EJOUR
T1 - SMART: A SENSOR-TRIGGERRED INTERACTIVE MR DISPLAY
AU - Y.S. Lan; C.D. Chen; S.W. Sun; W.C. Yen; Y.T. Wang; Y.H. Yang; J.M. Day; and K.L. Hua
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4678
ER -
Y.S. Lan, C.D. Chen, S.W. Sun, W.C. Yen, Y.T. Wang, Y.H. Yang, J.M. Day, and K.L. Hua. (2019). SMART: A SENSOR-TRIGGERRED INTERACTIVE MR DISPLAY. IEEE SigPort. http://sigport.org/4678
Y.S. Lan, C.D. Chen, S.W. Sun, W.C. Yen, Y.T. Wang, Y.H. Yang, J.M. Day, and K.L. Hua, 2019. SMART: A SENSOR-TRIGGERRED INTERACTIVE MR DISPLAY. Available at: http://sigport.org/4678.
Y.S. Lan, C.D. Chen, S.W. Sun, W.C. Yen, Y.T. Wang, Y.H. Yang, J.M. Day, and K.L. Hua. (2019). "SMART: A SENSOR-TRIGGERRED INTERACTIVE MR DISPLAY." Web.
1. Y.S. Lan, C.D. Chen, S.W. Sun, W.C. Yen, Y.T. Wang, Y.H. Yang, J.M. Day, and K.L. Hua. SMART: A SENSOR-TRIGGERRED INTERACTIVE MR DISPLAY [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4678

BAYESIAN-OPTIMIZED BIDIRECTIONAL LSTM REGRESSION MODEL FOR NON-INTRUSIVE LOAD MONITORING


In this paper, a Bayesian-optimized bidirectional Long Short -Term Memory (LSTM) method for energy disaggregation, is introduced. Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), is a process aiming to identify the individual contribution of appliances in the aggregate electricity load. The proposed model, Bayes-BiLSTM, is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increase.

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Authors:
Maria Kaselimi, Nikolaos Doulamis, Anastasios Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis
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10 May 2019 - 4:49pm
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[1] Maria Kaselimi, Nikolaos Doulamis, Anastasios Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis, "BAYESIAN-OPTIMIZED BIDIRECTIONAL LSTM REGRESSION MODEL FOR NON-INTRUSIVE LOAD MONITORING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4407. Accessed: Jul. 09, 2020.
@article{4407-19,
url = {http://sigport.org/4407},
author = {Maria Kaselimi; Nikolaos Doulamis; Anastasios Doulamis; Athanasios Voulodimos; Eftychios Protopapadakis },
publisher = {IEEE SigPort},
title = {BAYESIAN-OPTIMIZED BIDIRECTIONAL LSTM REGRESSION MODEL FOR NON-INTRUSIVE LOAD MONITORING},
year = {2019} }
TY - EJOUR
T1 - BAYESIAN-OPTIMIZED BIDIRECTIONAL LSTM REGRESSION MODEL FOR NON-INTRUSIVE LOAD MONITORING
AU - Maria Kaselimi; Nikolaos Doulamis; Anastasios Doulamis; Athanasios Voulodimos; Eftychios Protopapadakis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4407
ER -
Maria Kaselimi, Nikolaos Doulamis, Anastasios Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis. (2019). BAYESIAN-OPTIMIZED BIDIRECTIONAL LSTM REGRESSION MODEL FOR NON-INTRUSIVE LOAD MONITORING. IEEE SigPort. http://sigport.org/4407
Maria Kaselimi, Nikolaos Doulamis, Anastasios Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis, 2019. BAYESIAN-OPTIMIZED BIDIRECTIONAL LSTM REGRESSION MODEL FOR NON-INTRUSIVE LOAD MONITORING. Available at: http://sigport.org/4407.
Maria Kaselimi, Nikolaos Doulamis, Anastasios Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis. (2019). "BAYESIAN-OPTIMIZED BIDIRECTIONAL LSTM REGRESSION MODEL FOR NON-INTRUSIVE LOAD MONITORING." Web.
1. Maria Kaselimi, Nikolaos Doulamis, Anastasios Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis. BAYESIAN-OPTIMIZED BIDIRECTIONAL LSTM REGRESSION MODEL FOR NON-INTRUSIVE LOAD MONITORING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4407

Detecting and classifying rail corrugation based on axle bearing vibration

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Authors:
Andreas Fuchs
Submitted On:
9 May 2019 - 5:02am
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Poster_ICASSP_Alten_Fuchs.pdf

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[1] Andreas Fuchs, "Detecting and classifying rail corrugation based on axle bearing vibration", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4174. Accessed: Jul. 09, 2020.
@article{4174-19,
url = {http://sigport.org/4174},
author = {Andreas Fuchs },
publisher = {IEEE SigPort},
title = {Detecting and classifying rail corrugation based on axle bearing vibration},
year = {2019} }
TY - EJOUR
T1 - Detecting and classifying rail corrugation based on axle bearing vibration
AU - Andreas Fuchs
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4174
ER -
Andreas Fuchs. (2019). Detecting and classifying rail corrugation based on axle bearing vibration. IEEE SigPort. http://sigport.org/4174
Andreas Fuchs, 2019. Detecting and classifying rail corrugation based on axle bearing vibration. Available at: http://sigport.org/4174.
Andreas Fuchs. (2019). "Detecting and classifying rail corrugation based on axle bearing vibration." Web.
1. Andreas Fuchs. Detecting and classifying rail corrugation based on axle bearing vibration [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4174

Reconfigurable Multitask Audio Dynamics Processing Scheme


Automatic speech recognition (ASR), audio quality, and loudness are key performance indicators (KPIs) in smart speakers. To improve all these KPIs, audio dynamics processing is a crucial component in related systems. Unfortunately, single-band and existing multiband dynamics processing (MBDP) schemes fail to maximize bass and loudness but even produce unwanted peaks, distortions, and nonlinear echo so that an optimized ASR performance cannot be achieved. It has been a goal in both industry and academia to find a better audio dynamics processing for mitigating these problems.

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Authors:
Jun Yang, Amit S. Chhetri, Carlo Murgia, Philip Hilmes
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7 May 2019 - 4:47pm
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MBDP_ICASSP2019.pdf

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[1] Jun Yang, Amit S. Chhetri, Carlo Murgia, Philip Hilmes, "Reconfigurable Multitask Audio Dynamics Processing Scheme", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3920. Accessed: Jul. 09, 2020.
@article{3920-19,
url = {http://sigport.org/3920},
author = {Jun Yang; Amit S. Chhetri; Carlo Murgia; Philip Hilmes },
publisher = {IEEE SigPort},
title = {Reconfigurable Multitask Audio Dynamics Processing Scheme},
year = {2019} }
TY - EJOUR
T1 - Reconfigurable Multitask Audio Dynamics Processing Scheme
AU - Jun Yang; Amit S. Chhetri; Carlo Murgia; Philip Hilmes
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3920
ER -
Jun Yang, Amit S. Chhetri, Carlo Murgia, Philip Hilmes. (2019). Reconfigurable Multitask Audio Dynamics Processing Scheme. IEEE SigPort. http://sigport.org/3920
Jun Yang, Amit S. Chhetri, Carlo Murgia, Philip Hilmes, 2019. Reconfigurable Multitask Audio Dynamics Processing Scheme. Available at: http://sigport.org/3920.
Jun Yang, Amit S. Chhetri, Carlo Murgia, Philip Hilmes. (2019). "Reconfigurable Multitask Audio Dynamics Processing Scheme." Web.
1. Jun Yang, Amit S. Chhetri, Carlo Murgia, Philip Hilmes. Reconfigurable Multitask Audio Dynamics Processing Scheme [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3920

MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT


The paper proposes an efficient signal processing system mainly consisting of an adaptation-based nonlinear echo cancellation (NLEC) layer and a joint perceptual subband residual echo suppression (SBRES) layer and noise reduction (SBNR) layer. The theoretical analyses, subjective and objective test results show that the proposed signal processing system can offer a significant improvement for automatic speech recognition and full-duplex voice communication performance in emerging artificial intelligence speakers.

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7 May 2019 - 5:08pm
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JunYang_ICASSP2018_v3.pdf

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[1] , "MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2748. Accessed: Jul. 09, 2020.
@article{2748-18,
url = {http://sigport.org/2748},
author = { },
publisher = {IEEE SigPort},
title = {MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT},
year = {2018} }
TY - EJOUR
T1 - MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2748
ER -
. (2018). MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT. IEEE SigPort. http://sigport.org/2748
, 2018. MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT. Available at: http://sigport.org/2748.
. (2018). "MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT." Web.
1. . MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2748

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