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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM


Multiple-object tracking (MOT) and classification are core technologies for processing moving point clouds in radar or lidar applications. For accurate object classification, the one-to-one association relationship between the model of each objects' motion (trackers) and the observation sequences including auxiliary features (e.g., radar cross section) is important.

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Authors:
Yuta Kawachi, Teppei Suzuki
Submitted On:
4 June 2020 - 7:59am
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ICASSP2020_A0_vert_ykawachi_submit_20200415_3.pdf

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[1] Yuta Kawachi, Teppei Suzuki, "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5452. Accessed: Jul. 10, 2020.
@article{5452-20,
url = {http://sigport.org/5452},
author = {Yuta Kawachi; Teppei Suzuki },
publisher = {IEEE SigPort},
title = {UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM},
year = {2020} }
TY - EJOUR
T1 - UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM
AU - Yuta Kawachi; Teppei Suzuki
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5452
ER -
Yuta Kawachi, Teppei Suzuki. (2020). UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. IEEE SigPort. http://sigport.org/5452
Yuta Kawachi, Teppei Suzuki, 2020. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. Available at: http://sigport.org/5452.
Yuta Kawachi, Teppei Suzuki. (2020). "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM." Web.
1. Yuta Kawachi, Teppei Suzuki. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5452

UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM


Multiple-object tracking (MOT) and classification are core technologies for processing moving point clouds in radar or lidar applications. For accurate object classification, the one-to-one association relationship between the model of each objects' motion (trackers) and the observation sequences including auxiliary features (e.g., radar cross section) is important.

Paper Details

Authors:
Yuta Kawachi, Teppei Suzuki
Submitted On:
4 June 2020 - 7:59am
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Event:
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ICASSP2020_A0_vert_ykawachi_submit_20200415_3.pdf

(19)

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[1] Yuta Kawachi, Teppei Suzuki, "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5451. Accessed: Jul. 10, 2020.
@article{5451-20,
url = {http://sigport.org/5451},
author = {Yuta Kawachi; Teppei Suzuki },
publisher = {IEEE SigPort},
title = {UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM},
year = {2020} }
TY - EJOUR
T1 - UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM
AU - Yuta Kawachi; Teppei Suzuki
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5451
ER -
Yuta Kawachi, Teppei Suzuki. (2020). UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. IEEE SigPort. http://sigport.org/5451
Yuta Kawachi, Teppei Suzuki, 2020. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. Available at: http://sigport.org/5451.
Yuta Kawachi, Teppei Suzuki. (2020). "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM." Web.
1. Yuta Kawachi, Teppei Suzuki. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5451

ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network


Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images. Despite the visual quality of these generated images, there is still room for improvement. In this fashion, the model is extended to further improve the perceptual quality of the images. We have designed a network architecture with a novel basic block to replace the one used by the original ESRGAN. Moreover, we introduce noise inputs to the generator network in order to exploit stochastic variation.

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3 June 2020 - 8:27am
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Presentation ICASSP 2020.pdf

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[1] , "ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5450. Accessed: Jul. 10, 2020.
@article{5450-20,
url = {http://sigport.org/5450},
author = { },
publisher = {IEEE SigPort},
title = {ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network},
year = {2020} }
TY - EJOUR
T1 - ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5450
ER -
. (2020). ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network. IEEE SigPort. http://sigport.org/5450
, 2020. ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network. Available at: http://sigport.org/5450.
. (2020). "ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network." Web.
1. . ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5450

Deliberation Model Based Two-Pass End-to-End Speech Recognition

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Authors:
Tara Sainath, Ruoming Pang, Rohit Prabhavalkar
Submitted On:
1 June 2020 - 3:13pm
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Deliberation Model Based Two-Pass End-to-End Speech Recognition (ICASSP 2020).pdf

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[1] Tara Sainath, Ruoming Pang, Rohit Prabhavalkar, "Deliberation Model Based Two-Pass End-to-End Speech Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5449. Accessed: Jul. 10, 2020.
@article{5449-20,
url = {http://sigport.org/5449},
author = {Tara Sainath; Ruoming Pang; Rohit Prabhavalkar },
publisher = {IEEE SigPort},
title = {Deliberation Model Based Two-Pass End-to-End Speech Recognition},
year = {2020} }
TY - EJOUR
T1 - Deliberation Model Based Two-Pass End-to-End Speech Recognition
AU - Tara Sainath; Ruoming Pang; Rohit Prabhavalkar
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5449
ER -
Tara Sainath, Ruoming Pang, Rohit Prabhavalkar. (2020). Deliberation Model Based Two-Pass End-to-End Speech Recognition. IEEE SigPort. http://sigport.org/5449
Tara Sainath, Ruoming Pang, Rohit Prabhavalkar, 2020. Deliberation Model Based Two-Pass End-to-End Speech Recognition. Available at: http://sigport.org/5449.
Tara Sainath, Ruoming Pang, Rohit Prabhavalkar. (2020). "Deliberation Model Based Two-Pass End-to-End Speech Recognition." Web.
1. Tara Sainath, Ruoming Pang, Rohit Prabhavalkar. Deliberation Model Based Two-Pass End-to-End Speech Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5449

PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES


Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc. In this work, we propose a neural architecture coupled with a parameterized structured loss function to learn segmental representations for the task of phoneme boundary detection. First, we evaluated our model when the spoken phonemes were not given as input.

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Authors:
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi
Submitted On:
1 June 2020 - 11:44am
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2020-05 - ICASSP - Phoneme Boundary Detection using Learnable Segmental Features.pdf

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[1] Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi, "PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5448. Accessed: Jul. 10, 2020.
@article{5448-20,
url = {http://sigport.org/5448},
author = {Felix Kreuk; Yaniv Sheena; Joseph Keshet; Yossi Adi },
publisher = {IEEE SigPort},
title = {PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES},
year = {2020} }
TY - EJOUR
T1 - PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES
AU - Felix Kreuk; Yaniv Sheena; Joseph Keshet; Yossi Adi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5448
ER -
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi. (2020). PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES. IEEE SigPort. http://sigport.org/5448
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi, 2020. PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES. Available at: http://sigport.org/5448.
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi. (2020). "PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES." Web.
1. Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi. PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5448

An Efficient Alternative to Network Pruning through Ensemble Learning


Convolutional Neural Networks (CNNs) currently represent the best tool for classification of image content. CNNs are trained in order to develop generalized expressions in form of unique features to distinguish different classes. During this process, one or more filter weights might develop the same or similar values. In this case, the redundant filters can be pruned without damaging accuracy.Unlike normal pruning methods, we investigate the possibility of replacing a full-sized convolutional neural network with an ensemble of its narrow versions.

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Authors:
Martin Poellot, Rui Zhang, André Kaup
Submitted On:
29 May 2020 - 8:29am
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20.04.13_ComboNet_16x9.pdf

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[1] Martin Poellot, Rui Zhang, André Kaup, "An Efficient Alternative to Network Pruning through Ensemble Learning", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5447. Accessed: Jul. 10, 2020.
@article{5447-20,
url = {http://sigport.org/5447},
author = {Martin Poellot; Rui Zhang; André Kaup },
publisher = {IEEE SigPort},
title = {An Efficient Alternative to Network Pruning through Ensemble Learning},
year = {2020} }
TY - EJOUR
T1 - An Efficient Alternative to Network Pruning through Ensemble Learning
AU - Martin Poellot; Rui Zhang; André Kaup
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5447
ER -
Martin Poellot, Rui Zhang, André Kaup. (2020). An Efficient Alternative to Network Pruning through Ensemble Learning. IEEE SigPort. http://sigport.org/5447
Martin Poellot, Rui Zhang, André Kaup, 2020. An Efficient Alternative to Network Pruning through Ensemble Learning. Available at: http://sigport.org/5447.
Martin Poellot, Rui Zhang, André Kaup. (2020). "An Efficient Alternative to Network Pruning through Ensemble Learning." Web.
1. Martin Poellot, Rui Zhang, André Kaup. An Efficient Alternative to Network Pruning through Ensemble Learning [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5447

Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments


Depression detection from speech continues to attract significant research attention but remains a major challenge, particularly when the speech is acquired from diverse smartphones in natural environments. Analysis methods based on vocal tract coordination have shown great promise in depression and cognitive impairment detection for quantifying relationships between features over time through eigenvalues of multi-scale cross-correlations.

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Authors:
Zhaocheng Huang, Julien Epps, Dale Joachim
Submitted On:
28 May 2020 - 10:57pm
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presentation slides

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[1] Zhaocheng Huang, Julien Epps, Dale Joachim, "Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5446. Accessed: Jul. 10, 2020.
@article{5446-20,
url = {http://sigport.org/5446},
author = {Zhaocheng Huang; Julien Epps; Dale Joachim },
publisher = {IEEE SigPort},
title = {Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments},
year = {2020} }
TY - EJOUR
T1 - Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments
AU - Zhaocheng Huang; Julien Epps; Dale Joachim
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5446
ER -
Zhaocheng Huang, Julien Epps, Dale Joachim. (2020). Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments. IEEE SigPort. http://sigport.org/5446
Zhaocheng Huang, Julien Epps, Dale Joachim, 2020. Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments. Available at: http://sigport.org/5446.
Zhaocheng Huang, Julien Epps, Dale Joachim. (2020). "Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments." Web.
1. Zhaocheng Huang, Julien Epps, Dale Joachim. Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5446

Approaching Optimal Embedding in Audio Steganography with GAN


Audio steganography is a technology that embeds messages into audio without raising any suspicion from hearing it. Current steganography methods are based on heuristic cost designs. In this work, we proposed a framework based on Generative Adversarial Network (GAN) to approach optimal embedding for audio steganography in the temporal domain. This is the first attempt to approach optimal embedding with GAN and automatically learn the embedding probability/cost for audio steganography.

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Authors:
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi
Submitted On:
28 May 2020 - 10:39pm
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icassp2020_2093.pdf

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[1] Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi, "Approaching Optimal Embedding in Audio Steganography with GAN", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5445. Accessed: Jul. 10, 2020.
@article{5445-20,
url = {http://sigport.org/5445},
author = {Jianhua Yang; Huilin Zheng; Xiangui Kang; Yun-Qing Shi },
publisher = {IEEE SigPort},
title = {Approaching Optimal Embedding in Audio Steganography with GAN},
year = {2020} }
TY - EJOUR
T1 - Approaching Optimal Embedding in Audio Steganography with GAN
AU - Jianhua Yang; Huilin Zheng; Xiangui Kang; Yun-Qing Shi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5445
ER -
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi. (2020). Approaching Optimal Embedding in Audio Steganography with GAN. IEEE SigPort. http://sigport.org/5445
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi, 2020. Approaching Optimal Embedding in Audio Steganography with GAN. Available at: http://sigport.org/5445.
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi. (2020). "Approaching Optimal Embedding in Audio Steganography with GAN." Web.
1. Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi. Approaching Optimal Embedding in Audio Steganography with GAN [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5445

Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision

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Authors:
Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous
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28 May 2020 - 1:01am
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ICASSP 2020_ Coincidence, Categorization, and Consolidation.pdf

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[1] Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous, "Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5444. Accessed: Jul. 10, 2020.
@article{5444-20,
url = {http://sigport.org/5444},
author = {Aren Jansen; Daniel P. W. Ellis; Shawn Hershey; R. Channing Moore; Manoj Plakal; Ashok Popat; Rif A. Saurous },
publisher = {IEEE SigPort},
title = {Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision},
year = {2020} }
TY - EJOUR
T1 - Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision
AU - Aren Jansen; Daniel P. W. Ellis; Shawn Hershey; R. Channing Moore; Manoj Plakal; Ashok Popat; Rif A. Saurous
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5444
ER -
Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous. (2020). Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision. IEEE SigPort. http://sigport.org/5444
Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous, 2020. Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision. Available at: http://sigport.org/5444.
Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous. (2020). "Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision." Web.
1. Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous. Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5444

Source Coding of Audio Signals with a Generative Model


These are the slides from the video presentation at ICASSP 2020 of the paper "Source Coding of Audio Signals with a Generative Model".

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Authors:
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou
Submitted On:
27 May 2020 - 2:04pm
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SourceCodingAudioSignalsGenerativeModel-ICASSP2020.pdf

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[1] Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou, "Source Coding of Audio Signals with a Generative Model", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5443. Accessed: Jul. 10, 2020.
@article{5443-20,
url = {http://sigport.org/5443},
author = {Roy Fejgin; Janusz Klejsa; Lars Villemoes; Cong Zhou },
publisher = {IEEE SigPort},
title = {Source Coding of Audio Signals with a Generative Model},
year = {2020} }
TY - EJOUR
T1 - Source Coding of Audio Signals with a Generative Model
AU - Roy Fejgin; Janusz Klejsa; Lars Villemoes; Cong Zhou
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5443
ER -
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou. (2020). Source Coding of Audio Signals with a Generative Model. IEEE SigPort. http://sigport.org/5443
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou, 2020. Source Coding of Audio Signals with a Generative Model. Available at: http://sigport.org/5443.
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou. (2020). "Source Coding of Audio Signals with a Generative Model." Web.
1. Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou. Source Coding of Audio Signals with a Generative Model [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5443

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