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IEEE TETCI 2017 2017 : Computational Intelligence for End-to-End Audio Processing - Special Issue of the IEEE Transactions on Emerging Topics in Computational Intelligence

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Link: http://a3lab.dibet.univpm.it/news/ieee-tetci-special-issue
 
When N/A
Where N/A
Submission Deadline May 5, 2017
Notification Due Aug 5, 2017
Final Version Due Nov 5, 2017
Categories    computational audio processing   end-to-end learning   computational intelligence   digital audio
 

Call For Papers

Special Issue of the IEEE TRANSACTIONS on Emerging Topics in Computational Intelligence



GUEST EDITORS

Stefano Squartini - Università Politecnica delle Marche, Italy
Björn Schuller - Imperial College London, U.K., and University of Passau, Germany
Aurelio Uncini - University La Sapienza, Italy
Chuan-Kang Ting - National Chung Cheng University, Taiwan



SCOPE

Computational Audio Processing techniques have been largely addressed by scientists and technicians in diverse application areas, like entertainment, human-machine interfaces, security, forensics, and health. Developed services in these fields are characterized by a progressive increase of complexity, interactivity and intelligence, and the employment of Computational Intelligence techniques allowed to achieve a remarkable degree of automation with excellent performance.

The typical methodology adopted in these tasks consists in extracting and manipulating useful information from the audio stream to pilot the execution of target services. Such an approach is applied to different kinds of audio signals, from music to speech, from sound to acoustic data, and for each of them we can easily identify specific research topics, some of which have already reached a high maturity level. 
In the last few years, a new emerging computational intelligence paradigm has become popular among scientists working in the field. Broadly named as end-to-end learning, it consists in omitting any hand-crafted intermediary algorithms in the solution of a given problem and directly learning all needed information from the sampled dataset. This means that features used as input of the parametric system to train are not selected by humans, but they are determined by the system itself during the learning process. Due to its flexibility and versatility, such an approach encountered a certain success in the Computational Audio Processing field, for all types of signals mentioned above.

For instance, deep neural architectures, like Convolutional Neural Networks, are often used 
in these contexts and fed with raw audio data in the time or frequency domains, whereas supervised, weakly-supervised or unsupervised training algorithms are responsible to find a suitable data representation across the different abstraction layers to solve the task under study, i.e. classification, recognition and detection. On the other side, an increasing attention has been given by the scientific community to the development of end-to-end solutions to synthesize raw audio streams, like speech or music. Generative Adversarial Networks and WaveNets are the most recent and performing examples for this kind of problems.

It is thus of great interest for the scientific community to understand how and to what extent novel Computational Intelligence techniques based on the emerging end-to-end learning paradigm can be efficiently employed in Digital Audio, in the light of all aforementioned aspects. In line with the mission of the IEEE Computational Intelligence Society Task Force in Computational Audio Processing (http://ieeeciscap.dii.univpm.it/), the organizers of this Special Issue want to bring the focus on the most recent advancements in the Computational Intelligence field and on their applicability to Digital Audio problems from the end-to-end learning perspective.




TOPICS

Workshop topics include, but are not limited to:

End-to-End Learning for Digital Audio Applications

Audio Feature Representation Learning

Computational Audio Analysis from Raw Data

Unsupervised Feature Extraction from Audio Signals

Bags of Audio Words in Audio Pattern Recognition

End-to-End Cross-domain Audio Analysis

Data-learnt Audio Feature Representations and Higher Level Audio Features

Automatic Feature Analysis for Sound Event Classification and Recognition

Transfer, Weakly Supervised, and Reinforcement Learning for Audio

End-to-End Neural Architectures for Music Information Retrieval

End-to-End Computational methods for Music/Speech Synthesis

Generative modelling techniques for Raw Audio

End-to-End Speech Recognition and Dialogue Systems





SUBMISSION GUIDELINES

Manuscripts should be prepared according to the “Information for Authors” section of the journal found at http://cis.ieee.org/ieee-transactions-on-emerging-topics-in-computational-intelligence.html and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “Computational Intelligence for End-to-End Audio Processing”, and clearly marking “Computational Intelligence for End-to-End audio Processing Special Issue Paper” as comments to the Editor-in-Chief.


IMPORTANT DATES (extended)

Deadline for manuscript submission: June 05, 2017

First Notification of Acceptance: September 05, 2017

Final manuscripts due: November 15, 2017

Publication of Special Issue: December 2017

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