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Audio and Acoustic Signal Processing

Universal Acoustic Using Neural Mixture Models

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Authors:
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu
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12 May 2019 - 2:42am
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UAM_v3.pdf

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[1] Amit Das, Jinyu Li, Yifan Gong, Changliang Lu, "Universal Acoustic Using Neural Mixture Models", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4458. Accessed: Jun. 26, 2019.
@article{4458-19,
url = {http://sigport.org/4458},
author = {Amit Das; Jinyu Li; Yifan Gong; Changliang Lu },
publisher = {IEEE SigPort},
title = {Universal Acoustic Using Neural Mixture Models},
year = {2019} }
TY - EJOUR
T1 - Universal Acoustic Using Neural Mixture Models
AU - Amit Das; Jinyu Li; Yifan Gong; Changliang Lu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4458
ER -
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu. (2019). Universal Acoustic Using Neural Mixture Models. IEEE SigPort. http://sigport.org/4458
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu, 2019. Universal Acoustic Using Neural Mixture Models. Available at: http://sigport.org/4458.
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu. (2019). "Universal Acoustic Using Neural Mixture Models." Web.
1. Amit Das, Jinyu Li, Yifan Gong, Changliang Lu. Universal Acoustic Using Neural Mixture Models [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4458

Geometric Invariants for Sparse Unknown View Tomography


In this paper, we study a 2D tomography problem for point source models with random unknown view angles. Rather than recovering the projection angles, we reconstruct the model through a set of rotation-invariant features that are estimated from the projection data. For a point source model, we show that these features reveal geometric information about the model such as the radial and pairwise distances. This establishes a connection between unknown view tomography and unassigned distance geometry problem (uDGP).

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Authors:
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao
Submitted On:
11 May 2019 - 7:56pm
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[1] Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao, "Geometric Invariants for Sparse Unknown View Tomography", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4456. Accessed: Jun. 26, 2019.
@article{4456-19,
url = {http://sigport.org/4456},
author = {Mona Zehni; Shuai Huang; Ivan Dokmanic; Zhizhen Zhao },
publisher = {IEEE SigPort},
title = {Geometric Invariants for Sparse Unknown View Tomography},
year = {2019} }
TY - EJOUR
T1 - Geometric Invariants for Sparse Unknown View Tomography
AU - Mona Zehni; Shuai Huang; Ivan Dokmanic; Zhizhen Zhao
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4456
ER -
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao. (2019). Geometric Invariants for Sparse Unknown View Tomography. IEEE SigPort. http://sigport.org/4456
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao, 2019. Geometric Invariants for Sparse Unknown View Tomography. Available at: http://sigport.org/4456.
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao. (2019). "Geometric Invariants for Sparse Unknown View Tomography." Web.
1. Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao. Geometric Invariants for Sparse Unknown View Tomography [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4456

ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS


A novel maximum likelihood trajectory estimation algorithm for targets in mixed stationary/moving conditions is presented. The proposed approach is able to estimate position and velocity of the target over arbitrary complex trajectories, while explicitly taking into account the possibility of stop&go motion. Moreover, a novel trajectory reconstruction method based on the theory of Bezier curve is developed for online smoothing of the trajectory, which keeps the advantages of Bayesian smoothing while introducing only a fixed lag in the estimation process.

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Authors:
Angelo Coluccia, Alessio Fascista, Giuseppe Ricci
Submitted On:
11 May 2019 - 12:32pm
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Poster ICASSP 2019

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[1] Angelo Coluccia, Alessio Fascista, Giuseppe Ricci, "ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4448. Accessed: Jun. 26, 2019.
@article{4448-19,
url = {http://sigport.org/4448},
author = {Angelo Coluccia; Alessio Fascista; Giuseppe Ricci },
publisher = {IEEE SigPort},
title = {ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS},
year = {2019} }
TY - EJOUR
T1 - ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS
AU - Angelo Coluccia; Alessio Fascista; Giuseppe Ricci
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4448
ER -
Angelo Coluccia, Alessio Fascista, Giuseppe Ricci. (2019). ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS. IEEE SigPort. http://sigport.org/4448
Angelo Coluccia, Alessio Fascista, Giuseppe Ricci, 2019. ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS. Available at: http://sigport.org/4448.
Angelo Coluccia, Alessio Fascista, Giuseppe Ricci. (2019). "ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS." Web.
1. Angelo Coluccia, Alessio Fascista, Giuseppe Ricci. ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4448

Deep Learning for Classroom Activity Detection from Audio


Increasingly, post-secondary instructors are incorporating innovative teaching practices into their classrooms to improve student learning outcomes. In order to assess the effect of these techniques, it is helpful to quantify the types of activity being conducted in the classroom. Unfortunately, self-reporting is unreliable and manual annotation is tedious and scales poorly.

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Authors:
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson
Submitted On:
10 May 2019 - 4:33pm
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[POSTER] Deep Learning for Classroom Activity Detection from Audio

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[1] Robin Cosbey, Allison Wusterbarth, Brian Hutchinson, "Deep Learning for Classroom Activity Detection from Audio", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4404. Accessed: Jun. 26, 2019.
@article{4404-19,
url = {http://sigport.org/4404},
author = {Robin Cosbey; Allison Wusterbarth; Brian Hutchinson },
publisher = {IEEE SigPort},
title = {Deep Learning for Classroom Activity Detection from Audio},
year = {2019} }
TY - EJOUR
T1 - Deep Learning for Classroom Activity Detection from Audio
AU - Robin Cosbey; Allison Wusterbarth; Brian Hutchinson
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4404
ER -
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson. (2019). Deep Learning for Classroom Activity Detection from Audio. IEEE SigPort. http://sigport.org/4404
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson, 2019. Deep Learning for Classroom Activity Detection from Audio. Available at: http://sigport.org/4404.
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson. (2019). "Deep Learning for Classroom Activity Detection from Audio." Web.
1. Robin Cosbey, Allison Wusterbarth, Brian Hutchinson. Deep Learning for Classroom Activity Detection from Audio [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4404

Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm


Back-propagation (BP) is now a classic learning paradigm
whose source of supervision is exclusively from the external
(input/output) nodes. Consequently, BP is easily vulnerable
to curse-of-depth in (very) Deep Learning Networks
(DLNs). This prompts us to advocate Internal Neuron’s
Learnablility (INL) with (1)internal teacher labels (ITL); and
(2)internal optimization metrics (IOM) for evaluating hidden
layers/nodes. Conceptually, INL is a step beyond the notion
of Internal Neuron’s Explainablility (INE), championed by

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Authors:
S. Y. Kung, Zejiang Hou, Yuchen Liu
Submitted On:
10 May 2019 - 2:03pm
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[1] S. Y. Kung, Zejiang Hou, Yuchen Liu, "Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4384. Accessed: Jun. 26, 2019.
@article{4384-19,
url = {http://sigport.org/4384},
author = {S. Y. Kung; Zejiang Hou; Yuchen Liu },
publisher = {IEEE SigPort},
title = {Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm},
year = {2019} }
TY - EJOUR
T1 - Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm
AU - S. Y. Kung; Zejiang Hou; Yuchen Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4384
ER -
S. Y. Kung, Zejiang Hou, Yuchen Liu. (2019). Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm. IEEE SigPort. http://sigport.org/4384
S. Y. Kung, Zejiang Hou, Yuchen Liu, 2019. Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm. Available at: http://sigport.org/4384.
S. Y. Kung, Zejiang Hou, Yuchen Liu. (2019). "Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm." Web.
1. S. Y. Kung, Zejiang Hou, Yuchen Liu. Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4384

Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints


Configuring hybrid precoders and combiners is the main challenge to be solved to operate at millimeter wave (mmWave) frequencies. The use of hybrid architectures imposse hardware constraints on the analog precoder that need to be carefully dealt with. In this paper, we develop hybrid precoders and combiners aiming at minimizing the Euclidean distance with respect to the approximate all-digital precoders and combiners maximizing the spectral efficiency under per-antenna power constraints.

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Authors:
Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic
Submitted On:
10 May 2019 - 1:42pm
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[1] Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic, "Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4380. Accessed: Jun. 26, 2019.
@article{4380-19,
url = {http://sigport.org/4380},
author = {Javier Rodriguez-Fernandez; Roberto Lopez-Valcarce; Nuria Gonzalez-Prelcic },
publisher = {IEEE SigPort},
title = {Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints},
year = {2019} }
TY - EJOUR
T1 - Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints
AU - Javier Rodriguez-Fernandez; Roberto Lopez-Valcarce; Nuria Gonzalez-Prelcic
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4380
ER -
Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic. (2019). Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints. IEEE SigPort. http://sigport.org/4380
Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic, 2019. Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints. Available at: http://sigport.org/4380.
Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic. (2019). "Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints." Web.
1. Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic. Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4380

MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION

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Authors:
Jonah Casebeer, Zhepei Wang, Paris Smaragdis
Submitted On:
10 May 2019 - 1:41pm
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[1] Jonah Casebeer, Zhepei Wang, Paris Smaragdis, "MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4379. Accessed: Jun. 26, 2019.
@article{4379-19,
url = {http://sigport.org/4379},
author = {Jonah Casebeer; Zhepei Wang; Paris Smaragdis },
publisher = {IEEE SigPort},
title = {MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION},
year = {2019} }
TY - EJOUR
T1 - MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION
AU - Jonah Casebeer; Zhepei Wang; Paris Smaragdis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4379
ER -
Jonah Casebeer, Zhepei Wang, Paris Smaragdis. (2019). MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION. IEEE SigPort. http://sigport.org/4379
Jonah Casebeer, Zhepei Wang, Paris Smaragdis, 2019. MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION. Available at: http://sigport.org/4379.
Jonah Casebeer, Zhepei Wang, Paris Smaragdis. (2019). "MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION." Web.
1. Jonah Casebeer, Zhepei Wang, Paris Smaragdis. MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4379

UNSUPERVISED TRAINING OF A DEEP CLUSTERING MODEL FOR MULTICHANNEL BLIND SOURCE SEPARATION


We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on deep clustering requires strong supervision and elaborate on why this is a limitation.

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Authors:
Lukas Drude, Daniel Hasenklever, Reinhold Haeb-Umbach
Submitted On:
10 May 2019 - 12:34pm
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[1] Lukas Drude, Daniel Hasenklever, Reinhold Haeb-Umbach, "UNSUPERVISED TRAINING OF A DEEP CLUSTERING MODEL FOR MULTICHANNEL BLIND SOURCE SEPARATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4371. Accessed: Jun. 26, 2019.
@article{4371-19,
url = {http://sigport.org/4371},
author = {Lukas Drude; Daniel Hasenklever; Reinhold Haeb-Umbach },
publisher = {IEEE SigPort},
title = {UNSUPERVISED TRAINING OF A DEEP CLUSTERING MODEL FOR MULTICHANNEL BLIND SOURCE SEPARATION},
year = {2019} }
TY - EJOUR
T1 - UNSUPERVISED TRAINING OF A DEEP CLUSTERING MODEL FOR MULTICHANNEL BLIND SOURCE SEPARATION
AU - Lukas Drude; Daniel Hasenklever; Reinhold Haeb-Umbach
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4371
ER -
Lukas Drude, Daniel Hasenklever, Reinhold Haeb-Umbach. (2019). UNSUPERVISED TRAINING OF A DEEP CLUSTERING MODEL FOR MULTICHANNEL BLIND SOURCE SEPARATION. IEEE SigPort. http://sigport.org/4371
Lukas Drude, Daniel Hasenklever, Reinhold Haeb-Umbach, 2019. UNSUPERVISED TRAINING OF A DEEP CLUSTERING MODEL FOR MULTICHANNEL BLIND SOURCE SEPARATION. Available at: http://sigport.org/4371.
Lukas Drude, Daniel Hasenklever, Reinhold Haeb-Umbach. (2019). "UNSUPERVISED TRAINING OF A DEEP CLUSTERING MODEL FOR MULTICHANNEL BLIND SOURCE SEPARATION." Web.
1. Lukas Drude, Daniel Hasenklever, Reinhold Haeb-Umbach. UNSUPERVISED TRAINING OF A DEEP CLUSTERING MODEL FOR MULTICHANNEL BLIND SOURCE SEPARATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4371

HMM-based Approaches to Model Multichannel Information in Sign Language inspired from Articulatory Features-based Speech Processing

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Authors:
Marzieh Razavi, N. Cihan Camgöz, Richard Bowden and Mathew Magimai.-Doss
Submitted On:
10 May 2019 - 11:21am
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[1] Marzieh Razavi, N. Cihan Camgöz, Richard Bowden and Mathew Magimai.-Doss , "HMM-based Approaches to Model Multichannel Information in Sign Language inspired from Articulatory Features-based Speech Processing", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4363. Accessed: Jun. 26, 2019.
@article{4363-19,
url = {http://sigport.org/4363},
author = {Marzieh Razavi; N. Cihan Camgöz; Richard Bowden and Mathew Magimai.-Doss },
publisher = {IEEE SigPort},
title = {HMM-based Approaches to Model Multichannel Information in Sign Language inspired from Articulatory Features-based Speech Processing},
year = {2019} }
TY - EJOUR
T1 - HMM-based Approaches to Model Multichannel Information in Sign Language inspired from Articulatory Features-based Speech Processing
AU - Marzieh Razavi; N. Cihan Camgöz; Richard Bowden and Mathew Magimai.-Doss
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4363
ER -
Marzieh Razavi, N. Cihan Camgöz, Richard Bowden and Mathew Magimai.-Doss . (2019). HMM-based Approaches to Model Multichannel Information in Sign Language inspired from Articulatory Features-based Speech Processing. IEEE SigPort. http://sigport.org/4363
Marzieh Razavi, N. Cihan Camgöz, Richard Bowden and Mathew Magimai.-Doss , 2019. HMM-based Approaches to Model Multichannel Information in Sign Language inspired from Articulatory Features-based Speech Processing. Available at: http://sigport.org/4363.
Marzieh Razavi, N. Cihan Camgöz, Richard Bowden and Mathew Magimai.-Doss . (2019). "HMM-based Approaches to Model Multichannel Information in Sign Language inspired from Articulatory Features-based Speech Processing." Web.
1. Marzieh Razavi, N. Cihan Camgöz, Richard Bowden and Mathew Magimai.-Doss . HMM-based Approaches to Model Multichannel Information in Sign Language inspired from Articulatory Features-based Speech Processing [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4363

Sound event envelope estimation in polyphonic mixtures


Sound event detection is the task of identifying automatically the presence and temporal boundaries of sound events within an input audio stream. In the last years, deep learning methods have established themselves as the state-of-the-art approach for the task, using binary indicators during training to denote whether an event is active or inactive. However, such binary activity indicators do not fully describe the events, and estimating the envelope of the sounds could provide more precise modeling of their activity.

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Authors:
Annamaria Mesaros, Toni Heittola, Tuomas Virtanen, Maximo Cobos, Francesc J. Ferri
Submitted On:
10 May 2019 - 10:32am
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poster_ICASSP.pdf

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[1] Annamaria Mesaros, Toni Heittola, Tuomas Virtanen, Maximo Cobos, Francesc J. Ferri, "Sound event envelope estimation in polyphonic mixtures", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4342. Accessed: Jun. 26, 2019.
@article{4342-19,
url = {http://sigport.org/4342},
author = {Annamaria Mesaros; Toni Heittola; Tuomas Virtanen; Maximo Cobos; Francesc J. Ferri },
publisher = {IEEE SigPort},
title = {Sound event envelope estimation in polyphonic mixtures},
year = {2019} }
TY - EJOUR
T1 - Sound event envelope estimation in polyphonic mixtures
AU - Annamaria Mesaros; Toni Heittola; Tuomas Virtanen; Maximo Cobos; Francesc J. Ferri
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4342
ER -
Annamaria Mesaros, Toni Heittola, Tuomas Virtanen, Maximo Cobos, Francesc J. Ferri. (2019). Sound event envelope estimation in polyphonic mixtures. IEEE SigPort. http://sigport.org/4342
Annamaria Mesaros, Toni Heittola, Tuomas Virtanen, Maximo Cobos, Francesc J. Ferri, 2019. Sound event envelope estimation in polyphonic mixtures. Available at: http://sigport.org/4342.
Annamaria Mesaros, Toni Heittola, Tuomas Virtanen, Maximo Cobos, Francesc J. Ferri. (2019). "Sound event envelope estimation in polyphonic mixtures." Web.
1. Annamaria Mesaros, Toni Heittola, Tuomas Virtanen, Maximo Cobos, Francesc J. Ferri. Sound event envelope estimation in polyphonic mixtures [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4342

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