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Machine Learning for Signal Processing

PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE


Non-negative matrix factorization (NMF) approximates a given matrix as a product of two non-negative matrix factors. Multiplicative algorithms deliver reliable results, but they show slow convergence for high-dimensional data and may be stuck away from local minima. Gradient descent methods have better behavior, but only apply to smooth losses. For non-smooth losses such as the Kullback-Leibler (KL) loss, surprisingly, these methods are lacking.

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Authors:
Felipe Yanez, Francis Bach
Submitted On:
8 March 2017 - 8:23pm
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First-order method for non-negative matrix factorization with the Kullback-Leibler loss.

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[1] Felipe Yanez, Francis Bach, "PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1714. Accessed: Sep. 20, 2017.
@article{1714-17,
url = {http://sigport.org/1714},
author = {Felipe Yanez; Francis Bach },
publisher = {IEEE SigPort},
title = {PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE},
year = {2017} }
TY - EJOUR
T1 - PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE
AU - Felipe Yanez; Francis Bach
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1714
ER -
Felipe Yanez, Francis Bach. (2017). PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE. IEEE SigPort. http://sigport.org/1714
Felipe Yanez, Francis Bach, 2017. PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE. Available at: http://sigport.org/1714.
Felipe Yanez, Francis Bach. (2017). "PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE." Web.
1. Felipe Yanez, Francis Bach. PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1714

Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms


Ecologists can assess the health of wetlands by monitoring populations of animals such as Anurans (i.e., frogs and toads), which are sensitive to habitat changes. But, surveying anurans requires trained experts to identify species from the animals’ mating calls. This identification task can be streamlined by automation. To this end, we propose an automatic frog-call classification algorithm and a smartphone application that drastically simplify the monitoring of anuran populations.

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Authors:
Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro
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8 March 2017 - 7:34pm
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Tomasini - ICASSP 2017 - Poster

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[1] Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro, "Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1713. Accessed: Sep. 20, 2017.
@article{1713-17,
url = {http://sigport.org/1713},
author = {Katrina Smart; Ronaldo Menezes; Mark Bush; Eraldo Ribeiro },
publisher = {IEEE SigPort},
title = {Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms},
year = {2017} }
TY - EJOUR
T1 - Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms
AU - Katrina Smart; Ronaldo Menezes; Mark Bush; Eraldo Ribeiro
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1713
ER -
Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro. (2017). Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms. IEEE SigPort. http://sigport.org/1713
Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro, 2017. Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms. Available at: http://sigport.org/1713.
Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro. (2017). "Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms." Web.
1. Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro. Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1713

A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES


This paper demonstrates the ability to accurately detect the movement state of Madagascar hissing cockroaches equipped with a custom board containing a five degree of freedom inertial measurement unit. The cockroach moves freely through an unobstructed arena while wirelessly transmitting its accelerometer and gyroscope data. Multiple window sizes, features, and classifiers are assessed. An in-depth analysis of the classification results is performed to better understand the strengths and weaknesses of the classifier and feature set.

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Authors:
Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton
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5 March 2017 - 11:22pm
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ICASSP2017_final.pptx

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[1] Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton, "A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1644. Accessed: Sep. 20, 2017.
@article{1644-17,
url = {http://sigport.org/1644},
author = {Jeremy Cole; Farrokh Mohammadzadeh; Christopher Bollinger; Tahmid Latif; Alper Bozkurt; and Edgar Lobaton },
publisher = {IEEE SigPort},
title = {A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES},
year = {2017} }
TY - EJOUR
T1 - A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES
AU - Jeremy Cole; Farrokh Mohammadzadeh; Christopher Bollinger; Tahmid Latif; Alper Bozkurt; and Edgar Lobaton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1644
ER -
Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton. (2017). A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES. IEEE SigPort. http://sigport.org/1644
Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton, 2017. A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES. Available at: http://sigport.org/1644.
Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton. (2017). "A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES." Web.
1. Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton. A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1644

MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION

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Authors:
Afshin Abdi, Faramarz Fekri
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5 March 2017 - 1:36pm
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Stream_poster.pdf

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[1] Afshin Abdi, Faramarz Fekri, "MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1638. Accessed: Sep. 20, 2017.
@article{1638-17,
url = {http://sigport.org/1638},
author = {Afshin Abdi; Faramarz Fekri },
publisher = {IEEE SigPort},
title = {MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION},
year = {2017} }
TY - EJOUR
T1 - MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION
AU - Afshin Abdi; Faramarz Fekri
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1638
ER -
Afshin Abdi, Faramarz Fekri. (2017). MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION. IEEE SigPort. http://sigport.org/1638
Afshin Abdi, Faramarz Fekri, 2017. MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION. Available at: http://sigport.org/1638.
Afshin Abdi, Faramarz Fekri. (2017). "MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION." Web.
1. Afshin Abdi, Faramarz Fekri. MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1638

A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES


Affect prediction is a classical problem and has recently garnered special interest in multimedia applications. Affect prediction in movies is one such domain, potentially aiding the design as well as the impact analysis of movies.Given the large diversity in movies (such as different genres and languages), obtaining a comprehensive movie dataset for modeling affect is challenging while models trained on smaller datasets may not generalize. In this paper, we address the problem of continuous affect ratings with the availability of limited in-domain data resources.

SigPort.zip

Package icon SigPort.zip (102 downloads)

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Authors:
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan
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1 March 2017 - 9:17am
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SigPort.zip

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[1] Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan, "A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1554. Accessed: Sep. 20, 2017.
@article{1554-17,
url = {http://sigport.org/1554},
author = {Sabyasachee Baruah; Rahul Gupta; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES},
year = {2017} }
TY - EJOUR
T1 - A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES
AU - Sabyasachee Baruah; Rahul Gupta; Shrikanth Narayanan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1554
ER -
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan. (2017). A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES. IEEE SigPort. http://sigport.org/1554
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan, 2017. A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES. Available at: http://sigport.org/1554.
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan. (2017). "A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES." Web.
1. Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan. A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1554

Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification

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28 February 2017 - 4:29am
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ICASSP-Final.pdf

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[1] , "Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1492. Accessed: Sep. 20, 2017.
@article{1492-17,
url = {http://sigport.org/1492},
author = { },
publisher = {IEEE SigPort},
title = {Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification},
year = {2017} }
TY - EJOUR
T1 - Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1492
ER -
. (2017). Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification. IEEE SigPort. http://sigport.org/1492
, 2017. Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification. Available at: http://sigport.org/1492.
. (2017). "Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification." Web.
1. . Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1492

Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm


Extracting inherent patterns from large data using decompositions of
data matrix by a sampled subset of exemplars has found many applications
in machine learning. We propose a computationally efficient
algorithm for adaptive exemplar sampling, called fast exemplar selection
(FES). The proposed algorithm can be seen as an efficient
variant of the oASIS algorithm (Patel et al). FES iteratively selects incoherent
exemplars based on the exemplars that are already sampled.
This is done by ensuring that the selected exemplars forms a positive

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Authors:
Pulkit Sharma, Anil Kumar Sao
Submitted On:
28 February 2017 - 12:26am
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conference_poster_4.pdf

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[1] Pulkit Sharma, Anil Kumar Sao, "Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1474. Accessed: Sep. 20, 2017.
@article{1474-17,
url = {http://sigport.org/1474},
author = {Pulkit Sharma; Anil Kumar Sao },
publisher = {IEEE SigPort},
title = {Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm},
year = {2017} }
TY - EJOUR
T1 - Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm
AU - Pulkit Sharma; Anil Kumar Sao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1474
ER -
Pulkit Sharma, Anil Kumar Sao. (2017). Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm. IEEE SigPort. http://sigport.org/1474
Pulkit Sharma, Anil Kumar Sao, 2017. Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm. Available at: http://sigport.org/1474.
Pulkit Sharma, Anil Kumar Sao. (2017). "Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm." Web.
1. Pulkit Sharma, Anil Kumar Sao. Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1474

Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

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10 December 2016 - 3:39pm
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Slides_GlobalSIP.pdf

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[1] , "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1383. Accessed: Sep. 20, 2017.
@article{1383-16,
url = {http://sigport.org/1383},
author = { },
publisher = {IEEE SigPort},
title = {Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision},
year = {2016} }
TY - EJOUR
T1 - Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1383
ER -
. (2016). Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. IEEE SigPort. http://sigport.org/1383
, 2016. Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. Available at: http://sigport.org/1383.
. (2016). "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision." Web.
1. . Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1383

Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering


In recent years, nonnegative matrix factorization (NMF) attracts much attention in machine learning and signal processing fields due to its interpretability of data in a low dimensional subspace. For clustering problems, symmetric nonnegative matrix factorization (SNMF) as an extension of NMF factorizes the similarity matrix of data points directly and outperforms NMF when dealing with nonlinear data structure. However, the clustering results of SNMF is very sensitive to noisy data.

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Authors:
Tianxiang Gao, Sigurdur Olafsson, Songtao Lu
Submitted On:
6 December 2016 - 7:16pm
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conference_poster_6.pdf

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[1] Tianxiang Gao, Sigurdur Olafsson, Songtao Lu, "Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1378. Accessed: Sep. 20, 2017.
@article{1378-16,
url = {http://sigport.org/1378},
author = {Tianxiang Gao; Sigurdur Olafsson; Songtao Lu },
publisher = {IEEE SigPort},
title = {Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering},
year = {2016} }
TY - EJOUR
T1 - Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering
AU - Tianxiang Gao; Sigurdur Olafsson; Songtao Lu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1378
ER -
Tianxiang Gao, Sigurdur Olafsson, Songtao Lu. (2016). Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering. IEEE SigPort. http://sigport.org/1378
Tianxiang Gao, Sigurdur Olafsson, Songtao Lu, 2016. Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering. Available at: http://sigport.org/1378.
Tianxiang Gao, Sigurdur Olafsson, Songtao Lu. (2016). "Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering." Web.
1. Tianxiang Gao, Sigurdur Olafsson, Songtao Lu. Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1378

Recurrent neural networks for polyphonic sound event detection in real life recordings


RECURRENT NEURAL NETWORKS FOR POLYPHONIC SOUND EVENT DETECTION IN REAL LIFE RECORDINGS

Slides from the presentation held at ICASSP 2016 for the paper: Recurrent neural networks for polyphonic sound event detection in real life recordings

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Authors:
Heikki Huttunen, Tuomas Virtanen
Submitted On:
4 April 2016 - 9:45am
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ICASSP_2016_slides.pdf

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[1] Heikki Huttunen, Tuomas Virtanen, "Recurrent neural networks for polyphonic sound event detection in real life recordings", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1082. Accessed: Sep. 20, 2017.
@article{1082-16,
url = {http://sigport.org/1082},
author = {Heikki Huttunen; Tuomas Virtanen },
publisher = {IEEE SigPort},
title = {Recurrent neural networks for polyphonic sound event detection in real life recordings},
year = {2016} }
TY - EJOUR
T1 - Recurrent neural networks for polyphonic sound event detection in real life recordings
AU - Heikki Huttunen; Tuomas Virtanen
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1082
ER -
Heikki Huttunen, Tuomas Virtanen. (2016). Recurrent neural networks for polyphonic sound event detection in real life recordings. IEEE SigPort. http://sigport.org/1082
Heikki Huttunen, Tuomas Virtanen, 2016. Recurrent neural networks for polyphonic sound event detection in real life recordings. Available at: http://sigport.org/1082.
Heikki Huttunen, Tuomas Virtanen. (2016). "Recurrent neural networks for polyphonic sound event detection in real life recordings." Web.
1. Heikki Huttunen, Tuomas Virtanen. Recurrent neural networks for polyphonic sound event detection in real life recordings [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1082

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