Sorry, you need to enable JavaScript to visit this website.

Source Separation and Signal Enhancement

Low-Latency Real-Time Blind Source Separation for Hearing Aids Based on Time-Domain Implementation of Online Independent Vector Analysis with Truncation of Non-Causal Components


In this paper, we present a low-latency scheme for real-time blind source separation (BSS) based on online auxiliary-function-based independent vector analysis (AuxIVA). In many real-time audio ap- plications, especially hearing aids, low latency is highly desirable. Conventional frequency-domain BSS methods suffer from a delay caused by frame analysis. To reduce the delay, we implement sep- aration filters as multiple FIR filters in the time domain, which are converted from demixing matrices estimated by online AuxIVA in the frequency domain.

Paper Details

Authors:
Masahiro Sunohara, Chiho Haruta, Nobutaka Ono
Submitted On:
6 March 2017 - 11:00am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Low-latency real-time BSS for hearing aids

(56 downloads)

Keywords

Subscribe

[1] Masahiro Sunohara, Chiho Haruta, Nobutaka Ono, "Low-Latency Real-Time Blind Source Separation for Hearing Aids Based on Time-Domain Implementation of Online Independent Vector Analysis with Truncation of Non-Causal Components", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1651. Accessed: May. 24, 2017.
@article{1651-17,
url = {http://sigport.org/1651},
author = {Masahiro Sunohara; Chiho Haruta; Nobutaka Ono },
publisher = {IEEE SigPort},
title = {Low-Latency Real-Time Blind Source Separation for Hearing Aids Based on Time-Domain Implementation of Online Independent Vector Analysis with Truncation of Non-Causal Components},
year = {2017} }
TY - EJOUR
T1 - Low-Latency Real-Time Blind Source Separation for Hearing Aids Based on Time-Domain Implementation of Online Independent Vector Analysis with Truncation of Non-Causal Components
AU - Masahiro Sunohara; Chiho Haruta; Nobutaka Ono
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1651
ER -
Masahiro Sunohara, Chiho Haruta, Nobutaka Ono. (2017). Low-Latency Real-Time Blind Source Separation for Hearing Aids Based on Time-Domain Implementation of Online Independent Vector Analysis with Truncation of Non-Causal Components. IEEE SigPort. http://sigport.org/1651
Masahiro Sunohara, Chiho Haruta, Nobutaka Ono, 2017. Low-Latency Real-Time Blind Source Separation for Hearing Aids Based on Time-Domain Implementation of Online Independent Vector Analysis with Truncation of Non-Causal Components. Available at: http://sigport.org/1651.
Masahiro Sunohara, Chiho Haruta, Nobutaka Ono. (2017). "Low-Latency Real-Time Blind Source Separation for Hearing Aids Based on Time-Domain Implementation of Online Independent Vector Analysis with Truncation of Non-Causal Components." Web.
1. Masahiro Sunohara, Chiho Haruta, Nobutaka Ono. Low-Latency Real-Time Blind Source Separation for Hearing Aids Based on Time-Domain Implementation of Online Independent Vector Analysis with Truncation of Non-Causal Components [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1651

Learning complex-valued latent filters with absolute cosine similarity

Paper Details

Authors:
Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon
Submitted On:
5 March 2017 - 6:10pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icassp2017_slides.pdf

(23 downloads)

Keywords

Subscribe

[1] Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon, "Learning complex-valued latent filters with absolute cosine similarity", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1641. Accessed: May. 24, 2017.
@article{1641-17,
url = {http://sigport.org/1641},
author = {Anh H.T. Nguyen; V.G. Reju; Andy W.H. Khong; and Ing Yann Soon },
publisher = {IEEE SigPort},
title = {Learning complex-valued latent filters with absolute cosine similarity},
year = {2017} }
TY - EJOUR
T1 - Learning complex-valued latent filters with absolute cosine similarity
AU - Anh H.T. Nguyen; V.G. Reju; Andy W.H. Khong; and Ing Yann Soon
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1641
ER -
Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon. (2017). Learning complex-valued latent filters with absolute cosine similarity. IEEE SigPort. http://sigport.org/1641
Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon, 2017. Learning complex-valued latent filters with absolute cosine similarity. Available at: http://sigport.org/1641.
Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon. (2017). "Learning complex-valued latent filters with absolute cosine similarity." Web.
1. Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon. Learning complex-valued latent filters with absolute cosine similarity [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1641

Learning complex-valued latent filters with absolute cosine similarity


We propose a new sparse coding technique based on the power mean of phase-invariant cosine distances. Our approach is a generalization of sparse filtering and K-hyperlines clustering. It offers a better sparsity enforcer than the L1/L2 norm ratio that is typically used in sparse filtering. At the same time, the proposed approach scales better than the clustering counter parts for high-dimensional input. Our algorithm fully exploits the prior information obtained by preprocessing the observed data with whitening via an efficient row-wise decoupling scheme.

Paper Details

Authors:
Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon
Submitted On:
5 March 2017 - 6:10pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

AnhHTNguyen_icassp2017_poster.pdf

(22 downloads)

Keywords

Subscribe

[1] Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon, "Learning complex-valued latent filters with absolute cosine similarity", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1640. Accessed: May. 24, 2017.
@article{1640-17,
url = {http://sigport.org/1640},
author = {Anh H.T. Nguyen; V.G. Reju; Andy W.H. Khong; and Ing Yann Soon },
publisher = {IEEE SigPort},
title = {Learning complex-valued latent filters with absolute cosine similarity},
year = {2017} }
TY - EJOUR
T1 - Learning complex-valued latent filters with absolute cosine similarity
AU - Anh H.T. Nguyen; V.G. Reju; Andy W.H. Khong; and Ing Yann Soon
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1640
ER -
Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon. (2017). Learning complex-valued latent filters with absolute cosine similarity. IEEE SigPort. http://sigport.org/1640
Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon, 2017. Learning complex-valued latent filters with absolute cosine similarity. Available at: http://sigport.org/1640.
Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon. (2017). "Learning complex-valued latent filters with absolute cosine similarity." Web.
1. Anh H.T. Nguyen, V.G. Reju, Andy W.H. Khong, and Ing Yann Soon. Learning complex-valued latent filters with absolute cosine similarity [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1640

A NEURAL NETWORK ALTERNATIVE TO NON-NEGATIVE AUDIO MODELS

Paper Details

Authors:
Paris Smaragdis, Shrikant Venkataramani
Submitted On:
5 March 2017 - 11:16am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_2017.pdf

(31 downloads)

Keywords

Subscribe

[1] Paris Smaragdis, Shrikant Venkataramani, "A NEURAL NETWORK ALTERNATIVE TO NON-NEGATIVE AUDIO MODELS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1635. Accessed: May. 24, 2017.
@article{1635-17,
url = {http://sigport.org/1635},
author = {Paris Smaragdis; Shrikant Venkataramani },
publisher = {IEEE SigPort},
title = {A NEURAL NETWORK ALTERNATIVE TO NON-NEGATIVE AUDIO MODELS},
year = {2017} }
TY - EJOUR
T1 - A NEURAL NETWORK ALTERNATIVE TO NON-NEGATIVE AUDIO MODELS
AU - Paris Smaragdis; Shrikant Venkataramani
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1635
ER -
Paris Smaragdis, Shrikant Venkataramani. (2017). A NEURAL NETWORK ALTERNATIVE TO NON-NEGATIVE AUDIO MODELS. IEEE SigPort. http://sigport.org/1635
Paris Smaragdis, Shrikant Venkataramani, 2017. A NEURAL NETWORK ALTERNATIVE TO NON-NEGATIVE AUDIO MODELS. Available at: http://sigport.org/1635.
Paris Smaragdis, Shrikant Venkataramani. (2017). "A NEURAL NETWORK ALTERNATIVE TO NON-NEGATIVE AUDIO MODELS." Web.
1. Paris Smaragdis, Shrikant Venkataramani. A NEURAL NETWORK ALTERNATIVE TO NON-NEGATIVE AUDIO MODELS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1635

PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS


Phase-aware signal processing has received increasing interest
in many speech applications. The success of phase-aware
processing depends strongly on the robustness of the clean
spectral phase estimates to be obtained from a noisy observation.
In this paper, we propose a novel harmonic phase estimator
relying on the phase invariance property exploiting
relations between harmonics using the phase structure. We
present speech quality results achieved in speech enhancement
to justify the effectiveness of the proposed phase estimator

Paper Details

Authors:
Michael Pirolt, Johannes Stahl, Pejman Mowlaee, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov
Submitted On:
28 February 2017 - 10:46am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2017.pdf

(37 downloads)

Keywords

Subscribe

[1] Michael Pirolt, Johannes Stahl, Pejman Mowlaee, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov, "PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1512. Accessed: May. 24, 2017.
@article{1512-17,
url = {http://sigport.org/1512},
author = {Michael Pirolt; Johannes Stahl; Pejman Mowlaee; Vasili Vorobiov; Siarhei Barysenka; Andrew Davydov },
publisher = {IEEE SigPort},
title = {PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS},
year = {2017} }
TY - EJOUR
T1 - PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS
AU - Michael Pirolt; Johannes Stahl; Pejman Mowlaee; Vasili Vorobiov; Siarhei Barysenka; Andrew Davydov
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1512
ER -
Michael Pirolt, Johannes Stahl, Pejman Mowlaee, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov. (2017). PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS. IEEE SigPort. http://sigport.org/1512
Michael Pirolt, Johannes Stahl, Pejman Mowlaee, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov, 2017. PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS. Available at: http://sigport.org/1512.
Michael Pirolt, Johannes Stahl, Pejman Mowlaee, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov. (2017). "PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS." Web.
1. Michael Pirolt, Johannes Stahl, Pejman Mowlaee, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov. PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1512

Improving Music Source Separation based on DNNs through Data Augmentation and Network Blending


This paper deals with the separation of music into individual instrument tracks which is known to be a challenging problem. We describe two different deep neural network architectures for this task, a feed-forward and a recurrent one, and show that each of them yields themselves state-of-the art results on the SiSEC DSD100 dataset. For the recurrent network, we use data augmentation during training and show that even simple separation networks are prone to overfitting if no data augmentation is used.

Paper Details

Authors:
Stefan Uhlich, Marcello Porcu, Franck Giron, Michael Enenkl, Thomas Kemp, Naoya Takahashi, Yuki Mitsufuji
Submitted On:
28 February 2017 - 6:24am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_withmargin.pdf

(49 downloads)

Keywords

Subscribe

[1] Stefan Uhlich, Marcello Porcu, Franck Giron, Michael Enenkl, Thomas Kemp, Naoya Takahashi, Yuki Mitsufuji, "Improving Music Source Separation based on DNNs through Data Augmentation and Network Blending", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1502. Accessed: May. 24, 2017.
@article{1502-17,
url = {http://sigport.org/1502},
author = {Stefan Uhlich; Marcello Porcu; Franck Giron; Michael Enenkl; Thomas Kemp; Naoya Takahashi; Yuki Mitsufuji },
publisher = {IEEE SigPort},
title = {Improving Music Source Separation based on DNNs through Data Augmentation and Network Blending},
year = {2017} }
TY - EJOUR
T1 - Improving Music Source Separation based on DNNs through Data Augmentation and Network Blending
AU - Stefan Uhlich; Marcello Porcu; Franck Giron; Michael Enenkl; Thomas Kemp; Naoya Takahashi; Yuki Mitsufuji
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1502
ER -
Stefan Uhlich, Marcello Porcu, Franck Giron, Michael Enenkl, Thomas Kemp, Naoya Takahashi, Yuki Mitsufuji. (2017). Improving Music Source Separation based on DNNs through Data Augmentation and Network Blending. IEEE SigPort. http://sigport.org/1502
Stefan Uhlich, Marcello Porcu, Franck Giron, Michael Enenkl, Thomas Kemp, Naoya Takahashi, Yuki Mitsufuji, 2017. Improving Music Source Separation based on DNNs through Data Augmentation and Network Blending. Available at: http://sigport.org/1502.
Stefan Uhlich, Marcello Porcu, Franck Giron, Michael Enenkl, Thomas Kemp, Naoya Takahashi, Yuki Mitsufuji. (2017). "Improving Music Source Separation based on DNNs through Data Augmentation and Network Blending." Web.
1. Stefan Uhlich, Marcello Porcu, Franck Giron, Michael Enenkl, Thomas Kemp, Naoya Takahashi, Yuki Mitsufuji. Improving Music Source Separation based on DNNs through Data Augmentation and Network Blending [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1502

PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS

Paper Details

Authors:
Michael Pirolt, Johannes Stahl, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov
Submitted On:
11 March 2017 - 8:49pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2017.pdf

(67 downloads)

Keywords

Subscribe

[1] Michael Pirolt, Johannes Stahl, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov, "PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1485. Accessed: May. 24, 2017.
@article{1485-17,
url = {http://sigport.org/1485},
author = {Michael Pirolt; Johannes Stahl; Vasili Vorobiov; Siarhei Barysenka; Andrew Davydov },
publisher = {IEEE SigPort},
title = {PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS},
year = {2017} }
TY - EJOUR
T1 - PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS
AU - Michael Pirolt; Johannes Stahl; Vasili Vorobiov; Siarhei Barysenka; Andrew Davydov
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1485
ER -
Michael Pirolt, Johannes Stahl, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov. (2017). PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS. IEEE SigPort. http://sigport.org/1485
Michael Pirolt, Johannes Stahl, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov, 2017. PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS. Available at: http://sigport.org/1485.
Michael Pirolt, Johannes Stahl, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov. (2017). "PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS." Web.
1. Michael Pirolt, Johannes Stahl, Vasili Vorobiov, Siarhei Barysenka, Andrew Davydov. PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING PHASE INVARIANCE CONSTRAINTS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1485

LOW-LATENCY SOUND SOURCE SEPARATION USING DEEP NEURAL NETWORKS


Sound source separation at low-latency requires that each in- coming frame of audio data be processed at very low de- lay, and outputted as soon as possible. For practical pur- poses involving human listeners, a 20 ms algorithmic delay is the uppermost limit which is comfortable to the listener. In this paper, we propose a low-latency (algorithmic delay ≤ 20 ms) deep neural network (DNN) based source sepa- ration method.

Paper Details

Authors:
Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen
Submitted On:
8 December 2016 - 3:27pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP_poster2.pdf

(95 downloads)

Keywords

Subscribe

[1] Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen, "LOW-LATENCY SOUND SOURCE SEPARATION USING DEEP NEURAL NETWORKS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1426. Accessed: May. 24, 2017.
@article{1426-16,
url = {http://sigport.org/1426},
author = {Tom Barker; Niels Henrik Pontoppidan; Tuomas Virtanen },
publisher = {IEEE SigPort},
title = {LOW-LATENCY SOUND SOURCE SEPARATION USING DEEP NEURAL NETWORKS},
year = {2016} }
TY - EJOUR
T1 - LOW-LATENCY SOUND SOURCE SEPARATION USING DEEP NEURAL NETWORKS
AU - Tom Barker; Niels Henrik Pontoppidan; Tuomas Virtanen
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1426
ER -
Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen. (2016). LOW-LATENCY SOUND SOURCE SEPARATION USING DEEP NEURAL NETWORKS. IEEE SigPort. http://sigport.org/1426
Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen, 2016. LOW-LATENCY SOUND SOURCE SEPARATION USING DEEP NEURAL NETWORKS. Available at: http://sigport.org/1426.
Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen. (2016). "LOW-LATENCY SOUND SOURCE SEPARATION USING DEEP NEURAL NETWORKS." Web.
1. Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen. LOW-LATENCY SOUND SOURCE SEPARATION USING DEEP NEURAL NETWORKS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1426

Voice Activity Detection Based on Sequential Gaussian Mixture Model with Maximum Likelihood Criterion

Paper Details

Authors:
Zhan Shen, Jianguo Wei, Wenhuan Lu, Jianwu Dang
Submitted On:
15 October 2016 - 10:02am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ISCSLP2016_84.pptx

(68 downloads)

Keywords

Subscribe

[1] Zhan Shen, Jianguo Wei, Wenhuan Lu, Jianwu Dang, "Voice Activity Detection Based on Sequential Gaussian Mixture Model with Maximum Likelihood Criterion", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1237. Accessed: May. 24, 2017.
@article{1237-16,
url = {http://sigport.org/1237},
author = {Zhan Shen; Jianguo Wei; Wenhuan Lu; Jianwu Dang },
publisher = {IEEE SigPort},
title = {Voice Activity Detection Based on Sequential Gaussian Mixture Model with Maximum Likelihood Criterion},
year = {2016} }
TY - EJOUR
T1 - Voice Activity Detection Based on Sequential Gaussian Mixture Model with Maximum Likelihood Criterion
AU - Zhan Shen; Jianguo Wei; Wenhuan Lu; Jianwu Dang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1237
ER -
Zhan Shen, Jianguo Wei, Wenhuan Lu, Jianwu Dang. (2016). Voice Activity Detection Based on Sequential Gaussian Mixture Model with Maximum Likelihood Criterion. IEEE SigPort. http://sigport.org/1237
Zhan Shen, Jianguo Wei, Wenhuan Lu, Jianwu Dang, 2016. Voice Activity Detection Based on Sequential Gaussian Mixture Model with Maximum Likelihood Criterion. Available at: http://sigport.org/1237.
Zhan Shen, Jianguo Wei, Wenhuan Lu, Jianwu Dang. (2016). "Voice Activity Detection Based on Sequential Gaussian Mixture Model with Maximum Likelihood Criterion." Web.
1. Zhan Shen, Jianguo Wei, Wenhuan Lu, Jianwu Dang. Voice Activity Detection Based on Sequential Gaussian Mixture Model with Maximum Likelihood Criterion [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1237

Speech Enhancement with Binaural Cues Derived from a Priori Codebook


In conventional codebook-driven speech enhancement, only spectral envelopes of speech and noise are considered, and at the same time, the type of noise is the priori information when we enhance the noisy speech. In this paper, we propose a novel codebook-based speech enhancement method which exploits a priori information about binaural cues, including clean cue and pre-enhanced cue, stored in the trained codebook. This method includes two main parts: offline training of cues and online enhancement by means of cues.

Paper Details

Authors:
Nan Chen,Changchun Bao, Feng Deng
Submitted On:
13 October 2016 - 9:25pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ISLSLP2016 陈楠.ppt

(0)

Keywords

Subscribe

[1] Nan Chen,Changchun Bao, Feng Deng, "Speech Enhancement with Binaural Cues Derived from a Priori Codebook", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1152. Accessed: May. 24, 2017.
@article{1152-16,
url = {http://sigport.org/1152},
author = {Nan Chen;Changchun Bao; Feng Deng },
publisher = {IEEE SigPort},
title = {Speech Enhancement with Binaural Cues Derived from a Priori Codebook},
year = {2016} }
TY - EJOUR
T1 - Speech Enhancement with Binaural Cues Derived from a Priori Codebook
AU - Nan Chen;Changchun Bao; Feng Deng
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1152
ER -
Nan Chen,Changchun Bao, Feng Deng. (2016). Speech Enhancement with Binaural Cues Derived from a Priori Codebook. IEEE SigPort. http://sigport.org/1152
Nan Chen,Changchun Bao, Feng Deng, 2016. Speech Enhancement with Binaural Cues Derived from a Priori Codebook. Available at: http://sigport.org/1152.
Nan Chen,Changchun Bao, Feng Deng. (2016). "Speech Enhancement with Binaural Cues Derived from a Priori Codebook." Web.
1. Nan Chen,Changchun Bao, Feng Deng. Speech Enhancement with Binaural Cues Derived from a Priori Codebook [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1152

Pages