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ICASSP 2018

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 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.

RECOVERING SIGNALS FROM THEIR FROG TRACE

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Authors:
Tamir Bendory, Dan Edidin, Yonina Eldar
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12 April 2018 - 2:12pm
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FROG Poster v2.pdf

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[1] Tamir Bendory, Dan Edidin, Yonina Eldar , "RECOVERING SIGNALS FROM THEIR FROG TRACE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2465. Accessed: May. 26, 2019.
@article{2465-18,
url = {http://sigport.org/2465},
author = {Tamir Bendory; Dan Edidin; Yonina Eldar },
publisher = {IEEE SigPort},
title = {RECOVERING SIGNALS FROM THEIR FROG TRACE},
year = {2018} }
TY - EJOUR
T1 - RECOVERING SIGNALS FROM THEIR FROG TRACE
AU - Tamir Bendory; Dan Edidin; Yonina Eldar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2465
ER -
Tamir Bendory, Dan Edidin, Yonina Eldar . (2018). RECOVERING SIGNALS FROM THEIR FROG TRACE. IEEE SigPort. http://sigport.org/2465
Tamir Bendory, Dan Edidin, Yonina Eldar , 2018. RECOVERING SIGNALS FROM THEIR FROG TRACE. Available at: http://sigport.org/2465.
Tamir Bendory, Dan Edidin, Yonina Eldar . (2018). "RECOVERING SIGNALS FROM THEIR FROG TRACE." Web.
1. Tamir Bendory, Dan Edidin, Yonina Eldar . RECOVERING SIGNALS FROM THEIR FROG TRACE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2465

STATISTICAL LEARNING OF RATIONAL WAVELET TRANSFORM FOR NATURAL IMAGES


Motivated with the concept of transform learning and the utility of rational wavelet transform in audio and speech processing, this paper proposes Rational Wavelet Transform Learning in Statistical sense (RWLS) for natural images. The proposed RWLS design is carried out via lifting framework and is shown to have a closed form solution. The efficacy of the learned transform is demonstrated in the application of compressed sensing (CS) based reconstruction. The learned RWLS is observed to perform better than the existing standard dyadic wavelet transforms.

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12 April 2018 - 2:11pm
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ICASSP 2018.pdf

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[1] , "STATISTICAL LEARNING OF RATIONAL WAVELET TRANSFORM FOR NATURAL IMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2464. Accessed: May. 26, 2019.
@article{2464-18,
url = {http://sigport.org/2464},
author = { },
publisher = {IEEE SigPort},
title = {STATISTICAL LEARNING OF RATIONAL WAVELET TRANSFORM FOR NATURAL IMAGES},
year = {2018} }
TY - EJOUR
T1 - STATISTICAL LEARNING OF RATIONAL WAVELET TRANSFORM FOR NATURAL IMAGES
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2464
ER -
. (2018). STATISTICAL LEARNING OF RATIONAL WAVELET TRANSFORM FOR NATURAL IMAGES. IEEE SigPort. http://sigport.org/2464
, 2018. STATISTICAL LEARNING OF RATIONAL WAVELET TRANSFORM FOR NATURAL IMAGES. Available at: http://sigport.org/2464.
. (2018). "STATISTICAL LEARNING OF RATIONAL WAVELET TRANSFORM FOR NATURAL IMAGES." Web.
1. . STATISTICAL LEARNING OF RATIONAL WAVELET TRANSFORM FOR NATURAL IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2464

OPPORTUNISTIC SYNCHRONISATION OF MULTI-STATIC STARING ARRAY RADARS VIA TRACK-BEFORE-DETECT


In this work, we consider the problem of synchronising separately located transmitters and a staring array receiver that also has a local transmitter. The acknowledged benefits of using separate transmitters in active sensing are often undermined by the difficulty in accurate synchronisation of the receiver and the transmitters. In this work, we propose a solution that is based on measurements from non-cooperative objects in the illuminated region. We formulate the problem as parameter estimation in a state space model with individual transmitter channel data cubes as measurements.

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Authors:
Kimin Kim, Murat Uney, Bernard Mulgrew
Submitted On:
15 April 2018 - 12:07am
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ICASSP_2018_Kimin_Kim_Poster

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[1] Kimin Kim, Murat Uney, Bernard Mulgrew, "OPPORTUNISTIC SYNCHRONISATION OF MULTI-STATIC STARING ARRAY RADARS VIA TRACK-BEFORE-DETECT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2463. Accessed: May. 26, 2019.
@article{2463-18,
url = {http://sigport.org/2463},
author = {Kimin Kim; Murat Uney; Bernard Mulgrew },
publisher = {IEEE SigPort},
title = {OPPORTUNISTIC SYNCHRONISATION OF MULTI-STATIC STARING ARRAY RADARS VIA TRACK-BEFORE-DETECT},
year = {2018} }
TY - EJOUR
T1 - OPPORTUNISTIC SYNCHRONISATION OF MULTI-STATIC STARING ARRAY RADARS VIA TRACK-BEFORE-DETECT
AU - Kimin Kim; Murat Uney; Bernard Mulgrew
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2463
ER -
Kimin Kim, Murat Uney, Bernard Mulgrew. (2018). OPPORTUNISTIC SYNCHRONISATION OF MULTI-STATIC STARING ARRAY RADARS VIA TRACK-BEFORE-DETECT. IEEE SigPort. http://sigport.org/2463
Kimin Kim, Murat Uney, Bernard Mulgrew, 2018. OPPORTUNISTIC SYNCHRONISATION OF MULTI-STATIC STARING ARRAY RADARS VIA TRACK-BEFORE-DETECT. Available at: http://sigport.org/2463.
Kimin Kim, Murat Uney, Bernard Mulgrew. (2018). "OPPORTUNISTIC SYNCHRONISATION OF MULTI-STATIC STARING ARRAY RADARS VIA TRACK-BEFORE-DETECT." Web.
1. Kimin Kim, Murat Uney, Bernard Mulgrew. OPPORTUNISTIC SYNCHRONISATION OF MULTI-STATIC STARING ARRAY RADARS VIA TRACK-BEFORE-DETECT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2463

Wavelet-Based Reconstruction for Unlimited Sampling


Self-reset analog-to-digital converters (ADCs) allow for digitization of a signal with a high dynamic range. The reset action is equivalent to a modulo operation performed on the signal. We consider the problem of recovering the original signal from the measured modulo-operated signal. In our formulation, we assume that the underlying signal is Lipschitz continuous. The modulo-operated signal can be expressed as the sum of the original signal and a piecewise-constant signal that captures the transitions. The reconstruction requires estimating the piecewise-constant signal.

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Authors:
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula
Submitted On:
12 April 2018 - 2:06pm
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ICASSP_2018_Sunil.pdf

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[1] Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula, "Wavelet-Based Reconstruction for Unlimited Sampling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2461. Accessed: May. 26, 2019.
@article{2461-18,
url = {http://sigport.org/2461},
author = {Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula },
publisher = {IEEE SigPort},
title = {Wavelet-Based Reconstruction for Unlimited Sampling},
year = {2018} }
TY - EJOUR
T1 - Wavelet-Based Reconstruction for Unlimited Sampling
AU - Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2461
ER -
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. (2018). Wavelet-Based Reconstruction for Unlimited Sampling. IEEE SigPort. http://sigport.org/2461
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula, 2018. Wavelet-Based Reconstruction for Unlimited Sampling. Available at: http://sigport.org/2461.
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. (2018). "Wavelet-Based Reconstruction for Unlimited Sampling." Web.
1. Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. Wavelet-Based Reconstruction for Unlimited Sampling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2461

Wavelet-Based Reconstruction for Unlimited Sampling


Self-reset analog-to-digital converters (ADCs) allow for digitization of a signal with a high dynamic range. The reset action is equivalent to a modulo operation performed on the signal. We consider the problem of recovering the original signal from the measured modulo-operated signal. In our formulation, we assume that the underlying signal is Lipschitz continuous. The modulo-operated signal can be expressed as the sum of the original signal and a piecewise-constant signal that captures the transitions. The reconstruction requires estimating the piecewise-constant signal.

Paper Details

Authors:
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula
Submitted On:
12 April 2018 - 2:06pm
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Type:
Event:
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ICASSP_2018_Sunil.pdf

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[1] Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula, "Wavelet-Based Reconstruction for Unlimited Sampling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2460. Accessed: May. 26, 2019.
@article{2460-18,
url = {http://sigport.org/2460},
author = {Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula },
publisher = {IEEE SigPort},
title = {Wavelet-Based Reconstruction for Unlimited Sampling},
year = {2018} }
TY - EJOUR
T1 - Wavelet-Based Reconstruction for Unlimited Sampling
AU - Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2460
ER -
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. (2018). Wavelet-Based Reconstruction for Unlimited Sampling. IEEE SigPort. http://sigport.org/2460
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula, 2018. Wavelet-Based Reconstruction for Unlimited Sampling. Available at: http://sigport.org/2460.
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. (2018). "Wavelet-Based Reconstruction for Unlimited Sampling." Web.
1. Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. Wavelet-Based Reconstruction for Unlimited Sampling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2460

Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition


We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone phases which are no longer organized in a waterfall scheme. The whole system is realized by traversing backtrack arrows and cycles between various stages.

Paper Details

Authors:
Alessandro Tibo, Paolo Bientinesi
Submitted On:
12 April 2018 - 2:00pm
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Feature_Engineering_Pipeline

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[1] Alessandro Tibo, Paolo Bientinesi, "Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2459. Accessed: May. 26, 2019.
@article{2459-18,
url = {http://sigport.org/2459},
author = {Alessandro Tibo; Paolo Bientinesi },
publisher = {IEEE SigPort},
title = {Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition},
year = {2018} }
TY - EJOUR
T1 - Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition
AU - Alessandro Tibo; Paolo Bientinesi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2459
ER -
Alessandro Tibo, Paolo Bientinesi. (2018). Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition. IEEE SigPort. http://sigport.org/2459
Alessandro Tibo, Paolo Bientinesi, 2018. Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition. Available at: http://sigport.org/2459.
Alessandro Tibo, Paolo Bientinesi. (2018). "Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition." Web.
1. Alessandro Tibo, Paolo Bientinesi. Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2459

PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEO USING 3D CONVOLUTIONAL NEURAL NETWORKS


A 3-dimensional convolutional neural network is trained on unlabeled ultrasound video to predict an upcoming tongue image from previous ones. The network obtains results superior to those of simpler predictors, and provides a starting point for exploiting the higher-level representation of the tongue learned by the system in a variety of applications in speech research. This work is believed to be the first application of convolutional neural networks to unlabeled ultrasound video for the purpose of predicting tongue movement.

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Authors:
Shicheng Chen, Guorui Sheng, Pierre Roussel, Bruce Denby
Submitted On:
12 April 2018 - 1:53pm
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Poster

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[1] Shicheng Chen, Guorui Sheng, Pierre Roussel, Bruce Denby, "PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEO USING 3D CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2457. Accessed: May. 26, 2019.
@article{2457-18,
url = {http://sigport.org/2457},
author = {Shicheng Chen; Guorui Sheng; Pierre Roussel; Bruce Denby },
publisher = {IEEE SigPort},
title = {PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEO USING 3D CONVOLUTIONAL NEURAL NETWORKS},
year = {2018} }
TY - EJOUR
T1 - PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEO USING 3D CONVOLUTIONAL NEURAL NETWORKS
AU - Shicheng Chen; Guorui Sheng; Pierre Roussel; Bruce Denby
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2457
ER -
Shicheng Chen, Guorui Sheng, Pierre Roussel, Bruce Denby. (2018). PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEO USING 3D CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/2457
Shicheng Chen, Guorui Sheng, Pierre Roussel, Bruce Denby, 2018. PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEO USING 3D CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/2457.
Shicheng Chen, Guorui Sheng, Pierre Roussel, Bruce Denby. (2018). "PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEO USING 3D CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Shicheng Chen, Guorui Sheng, Pierre Roussel, Bruce Denby. PREDICTING TONGUE MOTION IN UNLABELED ULTRASOUND VIDEO USING 3D CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2457

FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR


This paper proposes a method of controlling the dynamic range compressor using sound examples. Our earlier work showed the effectiveness of random forest regression to map acoustic features to effect control parameters. We extend this work to address the challenging task of extracting relevant features when audio events overlap. We assess differ- ent audio decomposition approaches such as onset event detection, NMF, and transient/stationary audio separation using ISTA and compare feature extraction strategies for each case.

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Authors:
Di Sheng, Gyorgy Fazekas
Submitted On:
12 April 2018 - 1:40pm
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poster_icassp.pdf

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[1] Di Sheng, Gyorgy Fazekas, "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2456. Accessed: May. 26, 2019.
@article{2456-18,
url = {http://sigport.org/2456},
author = {Di Sheng; Gyorgy Fazekas },
publisher = {IEEE SigPort},
title = {FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR},
year = {2018} }
TY - EJOUR
T1 - FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR
AU - Di Sheng; Gyorgy Fazekas
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2456
ER -
Di Sheng, Gyorgy Fazekas. (2018). FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. IEEE SigPort. http://sigport.org/2456
Di Sheng, Gyorgy Fazekas, 2018. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. Available at: http://sigport.org/2456.
Di Sheng, Gyorgy Fazekas. (2018). "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR." Web.
1. Di Sheng, Gyorgy Fazekas. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2456

FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR


This paper proposes a method of controlling the dynamic range compressor using sound examples. Our earlier work showed the effectiveness of random forest regression to map acoustic features to effect control parameters. We extend this work to address the challenging task of extracting relevant features when audio events overlap. We assess differ- ent audio decomposition approaches such as onset event detection, NMF, and transient/stationary audio separation using ISTA and compare feature extraction strategies for each case.

Paper Details

Authors:
Di Sheng, Gyorgy Fazekas
Submitted On:
12 April 2018 - 1:40pm
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Type:
Event:
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poster_icassp.pdf

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[1] Di Sheng, Gyorgy Fazekas, "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2455. Accessed: May. 26, 2019.
@article{2455-18,
url = {http://sigport.org/2455},
author = {Di Sheng; Gyorgy Fazekas },
publisher = {IEEE SigPort},
title = {FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR},
year = {2018} }
TY - EJOUR
T1 - FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR
AU - Di Sheng; Gyorgy Fazekas
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2455
ER -
Di Sheng, Gyorgy Fazekas. (2018). FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. IEEE SigPort. http://sigport.org/2455
Di Sheng, Gyorgy Fazekas, 2018. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. Available at: http://sigport.org/2455.
Di Sheng, Gyorgy Fazekas. (2018). "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR." Web.
1. Di Sheng, Gyorgy Fazekas. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2455

FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR


This paper proposes a method of controlling the dynamic range compressor using sound examples. Our earlier work showed the effectiveness of random forest regression to map acoustic features to effect control parameters. We extend this work to address the challenging task of extracting relevant features when audio events overlap. We assess differ- ent audio decomposition approaches such as onset event detection, NMF, and transient/stationary audio separation using ISTA and compare feature extraction strategies for each case.

Paper Details

Authors:
Di Sheng, Gyorgy Fazekas
Submitted On:
12 April 2018 - 1:40pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

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poster_icassp.pdf

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[1] Di Sheng, Gyorgy Fazekas, "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2454. Accessed: May. 26, 2019.
@article{2454-18,
url = {http://sigport.org/2454},
author = {Di Sheng; Gyorgy Fazekas },
publisher = {IEEE SigPort},
title = {FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR},
year = {2018} }
TY - EJOUR
T1 - FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR
AU - Di Sheng; Gyorgy Fazekas
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2454
ER -
Di Sheng, Gyorgy Fazekas. (2018). FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. IEEE SigPort. http://sigport.org/2454
Di Sheng, Gyorgy Fazekas, 2018. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. Available at: http://sigport.org/2454.
Di Sheng, Gyorgy Fazekas. (2018). "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR." Web.
1. Di Sheng, Gyorgy Fazekas. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2454

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