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Speech Enhancement (SPE-ENHA)

TasNet: time-domain audio separation network for real-time, single-channel speech separation


Robust speech processing in multi-talker environments requires effective speech separation. Recent deep learning systems have made significant progress toward solving this problem, yet it remains challenging particularly in real-time, short latency applications. Most methods attempt to construct a mask for each source in time-frequency representation of the mixture signal which is not necessarily an optimal representation for speech separation.

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Authors:
Yi Luo, Nima Mesgarani
Submitted On:
19 April 2018 - 2:11pm
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ICASSP2018-poster.pdf

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[1] Yi Luo, Nima Mesgarani, "TasNet: time-domain audio separation network for real-time, single-channel speech separation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2987. Accessed: Jul. 16, 2018.
@article{2987-18,
url = {http://sigport.org/2987},
author = {Yi Luo; Nima Mesgarani },
publisher = {IEEE SigPort},
title = {TasNet: time-domain audio separation network for real-time, single-channel speech separation},
year = {2018} }
TY - EJOUR
T1 - TasNet: time-domain audio separation network for real-time, single-channel speech separation
AU - Yi Luo; Nima Mesgarani
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2987
ER -
Yi Luo, Nima Mesgarani. (2018). TasNet: time-domain audio separation network for real-time, single-channel speech separation. IEEE SigPort. http://sigport.org/2987
Yi Luo, Nima Mesgarani, 2018. TasNet: time-domain audio separation network for real-time, single-channel speech separation. Available at: http://sigport.org/2987.
Yi Luo, Nima Mesgarani. (2018). "TasNet: time-domain audio separation network for real-time, single-channel speech separation." Web.
1. Yi Luo, Nima Mesgarani. TasNet: time-domain audio separation network for real-time, single-channel speech separation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2987

A Study of Training Targets for Deep Neural Network-Based Speech Enhancement Using Noise Prediction

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Authors:
Babafemi O. Odelowo, David V. Anderson
Submitted On:
16 April 2018 - 12:07pm
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ICASSP_2018_Poster_Paper_4035v1.pdf

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[1] Babafemi O. Odelowo, David V. Anderson, "A Study of Training Targets for Deep Neural Network-Based Speech Enhancement Using Noise Prediction", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2890. Accessed: Jul. 16, 2018.
@article{2890-18,
url = {http://sigport.org/2890},
author = {Babafemi O. Odelowo; David V. Anderson },
publisher = {IEEE SigPort},
title = {A Study of Training Targets for Deep Neural Network-Based Speech Enhancement Using Noise Prediction},
year = {2018} }
TY - EJOUR
T1 - A Study of Training Targets for Deep Neural Network-Based Speech Enhancement Using Noise Prediction
AU - Babafemi O. Odelowo; David V. Anderson
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2890
ER -
Babafemi O. Odelowo, David V. Anderson. (2018). A Study of Training Targets for Deep Neural Network-Based Speech Enhancement Using Noise Prediction. IEEE SigPort. http://sigport.org/2890
Babafemi O. Odelowo, David V. Anderson, 2018. A Study of Training Targets for Deep Neural Network-Based Speech Enhancement Using Noise Prediction. Available at: http://sigport.org/2890.
Babafemi O. Odelowo, David V. Anderson. (2018). "A Study of Training Targets for Deep Neural Network-Based Speech Enhancement Using Noise Prediction." Web.
1. Babafemi O. Odelowo, David V. Anderson. A Study of Training Targets for Deep Neural Network-Based Speech Enhancement Using Noise Prediction [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2890

Speech Dereverberation based on Convex Optimization Algorithms for Group Sparse Linear Prediction


In this paper, we consider methods for improving far-field speech recognition using dereverberation based on sparse multi-channel linear prediction. In particular, we extend successful methods based on nonconvex iteratively reweighted least squares, that look for a sparse desired speech signal in the short-term Fourier transform domain, by proposing sparsity promoting convex functions. Furthermore, we show how to improve performance by applying regularization into both the reweighted least squares and convex methods.

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Authors:
Daniele Giacobello, Tobias Jensen
Submitted On:
12 April 2018 - 4:04pm
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poster_giacobello.pdf

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[1] Daniele Giacobello, Tobias Jensen, "Speech Dereverberation based on Convex Optimization Algorithms for Group Sparse Linear Prediction", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2491. Accessed: Jul. 16, 2018.
@article{2491-18,
url = {http://sigport.org/2491},
author = {Daniele Giacobello; Tobias Jensen },
publisher = {IEEE SigPort},
title = {Speech Dereverberation based on Convex Optimization Algorithms for Group Sparse Linear Prediction},
year = {2018} }
TY - EJOUR
T1 - Speech Dereverberation based on Convex Optimization Algorithms for Group Sparse Linear Prediction
AU - Daniele Giacobello; Tobias Jensen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2491
ER -
Daniele Giacobello, Tobias Jensen. (2018). Speech Dereverberation based on Convex Optimization Algorithms for Group Sparse Linear Prediction. IEEE SigPort. http://sigport.org/2491
Daniele Giacobello, Tobias Jensen, 2018. Speech Dereverberation based on Convex Optimization Algorithms for Group Sparse Linear Prediction. Available at: http://sigport.org/2491.
Daniele Giacobello, Tobias Jensen. (2018). "Speech Dereverberation based on Convex Optimization Algorithms for Group Sparse Linear Prediction." Web.
1. Daniele Giacobello, Tobias Jensen. Speech Dereverberation based on Convex Optimization Algorithms for Group Sparse Linear Prediction [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2491

TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK

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Authors:
Meet H. Soni, Neil Shah, Hemant A. Patil
Submitted On:
12 April 2018 - 12:43pm
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Prof. Hemant A Patil_ICASSP18.pdf

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[1] Meet H. Soni, Neil Shah, Hemant A. Patil, "TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2441. Accessed: Jul. 16, 2018.
@article{2441-18,
url = {http://sigport.org/2441},
author = {Meet H. Soni; Neil Shah; Hemant A. Patil },
publisher = {IEEE SigPort},
title = {TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK
AU - Meet H. Soni; Neil Shah; Hemant A. Patil
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2441
ER -
Meet H. Soni, Neil Shah, Hemant A. Patil. (2018). TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK. IEEE SigPort. http://sigport.org/2441
Meet H. Soni, Neil Shah, Hemant A. Patil, 2018. TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK. Available at: http://sigport.org/2441.
Meet H. Soni, Neil Shah, Hemant A. Patil. (2018). "TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK." Web.
1. Meet H. Soni, Neil Shah, Hemant A. Patil. TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2441

EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS


Many smart devices now support high-quality speech communication services at super-wide bandwidths. Often, however, speech quality is degraded when they are used with networks or devices which lack super-wideband support. Artificial bandwidth extension can then be used to improve speech quality. While approaches to wideband extension have been reported previously, this paper proposes an approach to super-wide bandwidth extension.

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12 April 2018 - 11:37am
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ICASSP2018_SWBE.pdf

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[1] , "EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2403. Accessed: Jul. 16, 2018.
@article{2403-18,
url = {http://sigport.org/2403},
author = { },
publisher = {IEEE SigPort},
title = {EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS},
year = {2018} }
TY - EJOUR
T1 - EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2403
ER -
. (2018). EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS. IEEE SigPort. http://sigport.org/2403
, 2018. EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS. Available at: http://sigport.org/2403.
. (2018). "EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS." Web.
1. . EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2403

EXPLOITING EXPLICIT MEMORY INCLUSION FOR ARTIFICIAL BANDWIDTH EXTENSION


Artificial bandwidth extension (ABE) algorithms have been developed to improve speech quality when wideband devices are used in conjunction with narrowband devices or infrastructure. While past work points to the benefit of using contextual information or memory for ABE, an understanding of the relative benefit of explicit memory inclusion, rather than just dynamic information, calls for a comparative, quantitative analysis. The need for practical ABE solutions calls further for the inclusion of memory without significant increases to latency or computational complexity.

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Authors:
Massimiliano Todisco, Nicholas Evans
Submitted On:
12 April 2018 - 11:35am
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ICASSP2018_ABE_memory.pdf

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[1] Massimiliano Todisco, Nicholas Evans, "EXPLOITING EXPLICIT MEMORY INCLUSION FOR ARTIFICIAL BANDWIDTH EXTENSION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2397. Accessed: Jul. 16, 2018.
@article{2397-18,
url = {http://sigport.org/2397},
author = {Massimiliano Todisco; Nicholas Evans },
publisher = {IEEE SigPort},
title = {EXPLOITING EXPLICIT MEMORY INCLUSION FOR ARTIFICIAL BANDWIDTH EXTENSION},
year = {2018} }
TY - EJOUR
T1 - EXPLOITING EXPLICIT MEMORY INCLUSION FOR ARTIFICIAL BANDWIDTH EXTENSION
AU - Massimiliano Todisco; Nicholas Evans
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2397
ER -
Massimiliano Todisco, Nicholas Evans. (2018). EXPLOITING EXPLICIT MEMORY INCLUSION FOR ARTIFICIAL BANDWIDTH EXTENSION. IEEE SigPort. http://sigport.org/2397
Massimiliano Todisco, Nicholas Evans, 2018. EXPLOITING EXPLICIT MEMORY INCLUSION FOR ARTIFICIAL BANDWIDTH EXTENSION. Available at: http://sigport.org/2397.
Massimiliano Todisco, Nicholas Evans. (2018). "EXPLOITING EXPLICIT MEMORY INCLUSION FOR ARTIFICIAL BANDWIDTH EXTENSION." Web.
1. Massimiliano Todisco, Nicholas Evans. EXPLOITING EXPLICIT MEMORY INCLUSION FOR ARTIFICIAL BANDWIDTH EXTENSION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2397

ICA BASED SINGLE MICROPHONE BLIND SPEECH SEPARATION TECHNIQUE USING NON-LINEAR ESTIMATION OF SPEECH


In this paper, a Blind Speech Separation (BSS) technique is introduced based on Independent Component Analysis (ICA) for underdetermined single microphone case. In general, ICA uses noisy speech from at least two microphones to separate speech and noise. But ICA fails to separate when only one stream of noisy speech is available. We use Log Spectral Magnitude Estimator based on Minimum Mean Square Error (LogMMSE) as a non-linear estimation technique to estimate the speech spectrum, which is used as the other input to ICA, with the noisy speech.

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Authors:
Anshuman Ganguly, Issa Panahi
Submitted On:
5 March 2017 - 11:37am
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ICASSP 2017-ICA SE Poster_edit_Final.pdf

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[1] Anshuman Ganguly, Issa Panahi, "ICA BASED SINGLE MICROPHONE BLIND SPEECH SEPARATION TECHNIQUE USING NON-LINEAR ESTIMATION OF SPEECH", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1637. Accessed: Jul. 16, 2018.
@article{1637-17,
url = {http://sigport.org/1637},
author = {Anshuman Ganguly; Issa Panahi },
publisher = {IEEE SigPort},
title = {ICA BASED SINGLE MICROPHONE BLIND SPEECH SEPARATION TECHNIQUE USING NON-LINEAR ESTIMATION OF SPEECH},
year = {2017} }
TY - EJOUR
T1 - ICA BASED SINGLE MICROPHONE BLIND SPEECH SEPARATION TECHNIQUE USING NON-LINEAR ESTIMATION OF SPEECH
AU - Anshuman Ganguly; Issa Panahi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1637
ER -
Anshuman Ganguly, Issa Panahi. (2017). ICA BASED SINGLE MICROPHONE BLIND SPEECH SEPARATION TECHNIQUE USING NON-LINEAR ESTIMATION OF SPEECH. IEEE SigPort. http://sigport.org/1637
Anshuman Ganguly, Issa Panahi, 2017. ICA BASED SINGLE MICROPHONE BLIND SPEECH SEPARATION TECHNIQUE USING NON-LINEAR ESTIMATION OF SPEECH. Available at: http://sigport.org/1637.
Anshuman Ganguly, Issa Panahi. (2017). "ICA BASED SINGLE MICROPHONE BLIND SPEECH SEPARATION TECHNIQUE USING NON-LINEAR ESTIMATION OF SPEECH." Web.
1. Anshuman Ganguly, Issa Panahi. ICA BASED SINGLE MICROPHONE BLIND SPEECH SEPARATION TECHNIQUE USING NON-LINEAR ESTIMATION OF SPEECH [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1637

SPEECH DEREVERBERATION USING NMF WITH REGULARIZED ROOM IMPULSE RESPONSE


In this paper, various regularizations on the room impulse response
(RIR) are proposed to obtain better single-channel speech derever-
beration in the non-negative matrix factorization (NMF) framework.
The regularizations on the RIR are motivated by the spectral domain
representation of the RIR. To obtain better estimates of the RIR and
clean speech, we propose three modifications (i) to obtain a sparse
RIR (ii) a frequency envelop constrained RIR and (iii) to include the
early part of the RIR. The performance of the proposed regularizers

Paper Details

Authors:
Nikhil Mohanan,Rajbabu Velmurugan,Preeti Rao
Submitted On:
2 March 2017 - 7:45am
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mohanan_icassp2017.pdf

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[1] Nikhil Mohanan,Rajbabu Velmurugan,Preeti Rao, "SPEECH DEREVERBERATION USING NMF WITH REGULARIZED ROOM IMPULSE RESPONSE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1584. Accessed: Jul. 16, 2018.
@article{1584-17,
url = {http://sigport.org/1584},
author = {Nikhil Mohanan;Rajbabu Velmurugan;Preeti Rao },
publisher = {IEEE SigPort},
title = {SPEECH DEREVERBERATION USING NMF WITH REGULARIZED ROOM IMPULSE RESPONSE},
year = {2017} }
TY - EJOUR
T1 - SPEECH DEREVERBERATION USING NMF WITH REGULARIZED ROOM IMPULSE RESPONSE
AU - Nikhil Mohanan;Rajbabu Velmurugan;Preeti Rao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1584
ER -
Nikhil Mohanan,Rajbabu Velmurugan,Preeti Rao. (2017). SPEECH DEREVERBERATION USING NMF WITH REGULARIZED ROOM IMPULSE RESPONSE. IEEE SigPort. http://sigport.org/1584
Nikhil Mohanan,Rajbabu Velmurugan,Preeti Rao, 2017. SPEECH DEREVERBERATION USING NMF WITH REGULARIZED ROOM IMPULSE RESPONSE. Available at: http://sigport.org/1584.
Nikhil Mohanan,Rajbabu Velmurugan,Preeti Rao. (2017). "SPEECH DEREVERBERATION USING NMF WITH REGULARIZED ROOM IMPULSE RESPONSE." Web.
1. Nikhil Mohanan,Rajbabu Velmurugan,Preeti Rao. SPEECH DEREVERBERATION USING NMF WITH REGULARIZED ROOM IMPULSE RESPONSE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1584

A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA


This paper provides a detailed comparison study between three different vehicles’ Bluetooth built-in noise cancellation filter with two widely used techniques in speech enhancement, Spectral Subtraction (SS) and Wiener filtering (WF). The main purpose is to determine if any of these two filters provide superior audio quality over the built-in filter.

Paper Details

Authors:
Sam Tabaja, Sai-Prithvi Gadde, Nabih Jaber, Philip Olivier, Mahdi Ali, Rakan Chabaan, Scott Bone
Submitted On:
6 December 2016 - 10:53pm
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Slides

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[1] Sam Tabaja, Sai-Prithvi Gadde, Nabih Jaber, Philip Olivier, Mahdi Ali, Rakan Chabaan, Scott Bone, "A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1392. Accessed: Jul. 16, 2018.
@article{1392-16,
url = {http://sigport.org/1392},
author = {Sam Tabaja; Sai-Prithvi Gadde; Nabih Jaber; Philip Olivier; Mahdi Ali; Rakan Chabaan; Scott Bone },
publisher = {IEEE SigPort},
title = {A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA},
year = {2016} }
TY - EJOUR
T1 - A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA
AU - Sam Tabaja; Sai-Prithvi Gadde; Nabih Jaber; Philip Olivier; Mahdi Ali; Rakan Chabaan; Scott Bone
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1392
ER -
Sam Tabaja, Sai-Prithvi Gadde, Nabih Jaber, Philip Olivier, Mahdi Ali, Rakan Chabaan, Scott Bone. (2016). A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA. IEEE SigPort. http://sigport.org/1392
Sam Tabaja, Sai-Prithvi Gadde, Nabih Jaber, Philip Olivier, Mahdi Ali, Rakan Chabaan, Scott Bone, 2016. A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA. Available at: http://sigport.org/1392.
Sam Tabaja, Sai-Prithvi Gadde, Nabih Jaber, Philip Olivier, Mahdi Ali, Rakan Chabaan, Scott Bone. (2016). "A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA." Web.
1. Sam Tabaja, Sai-Prithvi Gadde, Nabih Jaber, Philip Olivier, Mahdi Ali, Rakan Chabaan, Scott Bone. A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1392

A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA


This paper provides a detailed comparison study between three different vehicles’ Bluetooth built-in noise cancellation filter with two widely used techniques in speech enhancement, Spectral Subtraction (SS) and Wiener filtering (WF). The main purpose is to determine if any of these two filters provide superior audio quality over the built-in filter.

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Submitted On:
7 December 2016 - 10:44am
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SS_vs_WF_GlobalSIP16.pdf

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[1] , "A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1389. Accessed: Jul. 16, 2018.
@article{1389-16,
url = {http://sigport.org/1389},
author = { },
publisher = {IEEE SigPort},
title = {A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA},
year = {2016} }
TY - EJOUR
T1 - A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1389
ER -
. (2016). A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA. IEEE SigPort. http://sigport.org/1389
, 2016. A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA. Available at: http://sigport.org/1389.
. (2016). "A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA." Web.
1. . A QUANTITATIVE ANALYSIS OF HANDS-FREE SPEECH ENHANCEMENT USING REAL AUTOMOBILE DATA [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1389

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