Content Gazette - July 2019 - 15

MAY 2019

VOLUME 13

NUMBER 2

IJSTGY

(ISSN 1932-4553)

ISSUE ON DATA SCIENCE: MACHINE LEARNING FOR AUDIO SIGNAL PROCESSING
AWARDS

Article Awards for the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

202

EDITORIAL

Introduction to the Issue on Data Science: Machine Learning for Audio Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Purwins, B. Sturm, B. Li, J. Nam, and A. Alwan

203

PAPERS

Deep Learning for Audio Signal Processing . . . . . H. Purwins, B. Li, T. Virtanen, J. Schl¨uter, S.-Y. Chang, and T. Sainath
TimeScaleNet: A Multiresolution Approach for Raw Audio Recognition Using Learnable Biquadratic IIR Filters and
Residual Networks of Depthwise-Separable One-Dimensional Atrous Convolutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
´ Bavu, A. Ramamonjy, H. Pujol, and A. Garcia
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E.
Quality Control in Remote Speech Data Collection . . . . A. H. Poorjam, M. A. Little, J. R. Jensen, and M. G. Christensen
Modulation Filter Learning Using Deep Variational Networks for Robust Speech Recognition . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Agrawal and S. Ganapathy
Voice Activity Detection for Transient Noisy Environment Based on Diffusion Nets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Ivry, B. Berdugo, and I. Cohen
An End-to-End Multimodal Voice Activity Detection Using WaveNet Encoder and Residual Networks . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Ariav and I. Cohen
A Statistical Inference Framework for Understanding Music-Related Brain Activity . . . . S. Ntalampiras and I. Potamitis
Comparison and Analysis of SampleCNN Architectures for Audio Classification . . . . . . . . . . . T. Kim, J. Lee, and J. Nam
Deep Archetypal Analysis Based Intermediate Matching Kernel for Bioacoustic Classification . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Thakur and P. Rajan
Polyphonic Sound Event Detection by Using Capsule Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Vesperini, L. Gabrielli, E. Principi, and S. Squartini
A Subject-Driven Unsupervised Hidden Semi-Markov Model and Gaussian Mixture Model for Heart Sound
Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .J. Oliveira, F. Renna, and M. Coimbra
Unsupervised Low Latency Speech Enhancement With RT-GCC-NMF . . . . . . . . . . . . . . . . . . . S. U. N. Wood and J. Rouat
Adversarial Training for Speech Super-Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . S. E. Eskimez, K. Koishida, and Z. Duan
Monaural Source Separation in Complex Domain With Long Short-Term Memory Neural Network . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Sun, Y. Xian, W. Wang, and S. M. Naqvi
Phasebook and Friends: Leveraging Discrete Representations for Source Separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Le Roux, G. Wichern, S. Watanabe, A. Sarroff, and J. R. Hershey

370

Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

383

www.signalprocessingsociety.org

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https://standards.ieee.org/products-services/icap/index.html http://dx.doi.org/10.1109/JSTSP.2019.2908096 http://dx.doi.org/10.1109/JSTSP.2019.2914321 http://dx.doi.org/10.1109/JSTSP.2019.2908700 http://dx.doi.org/10.1109/JSTSP.2019.2908696 http://dx.doi.org/10.1109/JSTSP.2019.2908696 http://dx.doi.org/10.1109/JSTSP.2019.2904212 http://dx.doi.org/10.1109/JSTSP.2019.2913965 http://dx.doi.org/10.1109/JSTSP.2019.2909472 http://dx.doi.org/10.1109/JSTSP.2019.2901195 http://dx.doi.org/10.1109/JSTSP.2019.2905431 http://dx.doi.org/10.1109/JSTSP.2019.2909479 http://dx.doi.org/10.1109/JSTSP.2019.2906465 http://dx.doi.org/10.1109/JSTSP.2019.2902305 http://dx.doi.org/10.1109/JSTSP.2019.2908723 http://dx.doi.org/10.1109/JSTSP.2019.2908723 http://dx.doi.org/10.1109/JSTSP.2019.2909193 http://dx.doi.org/10.1109/JSTSP.2019.2909077 http://dx.doi.org/10.1109/JSTSP.2019.2908760 http://dx.doi.org/10.1109/JSTSP.2019.2904183 https://www.signalprocessingsociety.org/publications-resources/ieee-signal-processing-letters/information-authors-spl http://www.signalprocessingsociety.org

Content Gazette - July 2019

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Content Gazette - July 2019 - Cover3
Content Gazette - July 2019 - Cover4
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