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Call for Papers - Special Issue of IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE - Computational Intelligence for End-to-End Audio Processing

Dear Colleagues,


Please find below a call for a related special issue. Here is the link of the official Call for Papers.




***Apologies for multiple postings***





Computational Intelligence for

End-to-End Audio Processing


Special Issue of 












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





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 characterised 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 and across all a large variety of research areas. It is named end-to-end learning and 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 (like a Neural Network) 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 great interest in the Computational Audio Processing field, for all types of signals mentioned above. For instance, deep neural architectures are often adopted in these contexts and fed with raw audio data in the time or frequency domains, whereas the supervised, weakly-supervised or unsupervised training algorithms involved in the process 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 interest has been registered by the scientific community in the development of end-to-end solutions to synthesise 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 indeed 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 CIS Task Force in Computational Audio Processing (http://ieeeciscap.dii.univpm.it/) the organisers 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. 





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 





Electronic submissions for the Neurocomputing journal can be found under https://mc.manuscriptcentral.com/tetci-ieee

During the submission process, please choose Article Type as SI: CAP




  • Deadline for manuscript submission: May 05, 2017
  • First Notification of Acceptance: August 05, 2017
  • Final manuscripts due: November 05, 2017
  • Publication of Special Issue: December 2017










Univ.-Prof. Dr.-Ing. habil.

Björn W. Schuller


Head (Full Professor)

Chair of Complex and Intelligent Systems

University of Passau

Passau / Germany


Reader (Associate Professor)

Department of Computing

Imperial College London

London / U.K.




Gilching / Germany


Visiting Professor

School of Computer Science and Technology

Harbin Institute of Technology

Harbin / P.R. China



Institute for Information and Communication Technologies

Joanneum Research

Graz / Austria



Centre Interfacultaire en Sciences Affectives

Université de Genève

Geneva / Switzerland


Editor in Chief

IEEE Transactions on Affective Computing