Special Issue on Advanced Deep Learning Methods for Audio and Speech processing

Submission Deadline: Feb. 20, 2020

This special issue currently is open for paper submission and guest editor application.

  • Special Issue Editor
    • Peyman Goli
      Department of Electrical and Computer Engineering, Khavaran Institute of Higher Education, Mashhad, Iran
    Guest Editors play a significant role in a special issue. They maintain the quality of published research and enhance the special issue’s impact. If you would like to be a Guest Editor or recommend a colleague as a Guest Editor of this special issue, please Click here to fulfill the Guest Editor application.
    • Stuart Rubin
      Department of Applied Science and Technology / Intelligent Sensing Branch, Space and Naval Warfare Systems Center Pacific, San Diego, CA, USA
    • Nanlin Jin
      Department of Computer and Information Science, Northumbria University, Newcastle Upon Tyne, Tyne and Wear, UK
    • Ping Guo
      Department of Computer Science, University of Illinois at Springfield, Springfield, USA
    • Mohammad-Reza Karami
      Department of Electrical and Computer Engineering, Babol Noshivani University of Technology, Babol, Iran
    • Pollyana Notargiacomo
      School of Computing and Informatics, Mackenzie Presbyterian University, São Paulo, Brazil
    • Sangho Park
      Department of Computer, Electronics and Graphics Technology, Central Connecticut State University, New Britain, USA
  • Introduction

    Deep learning algorithms are widespread in audio and speech processing methods, from the state-of-the-art researches to the applications on our smartphones. This growing deployment of deep leaning-based audio and speech processing algorithms would not have been possible if not for the lightning-fast progress of computer science in both hardware and software aspects.

    Recently, employing deep learning in audio and speech processing approaches has shown significant improvement in system performance compared to the signal processing methods applying conventional machine learning algorithms. Automatic feature engineering in deep learning algorithms make them so compatible for learning the representations of audio and speech signals and creating a complex mapping between acoustic features and targets. Currently, deep neural networks have a wide-range application in audio and speech processing methods such as automatic speech recognition, speech enhancement, speech intelligibility improvement, multi-talker localization, noise PSD estimation, attended speech identification, beam forming, hearing aid development, and acoustic echo cancelation.

    An even more interesting research trend is focusing on new strategies for deep neural network based speech processing methods such as new stage-of-the-art combinations (i.e. deep neural network and hidden Markov model combinations) and multi-task learning models.

    The authors are encouraged to submit original research articles, reviews, theoretical and critical perspectives, and viewpoint articles in the fields of advanced deep learning methods for audio and speech processing.

    Aims and Scope:

    1. Automatic speech recognition
    2. Deep neural network based speech enhancement
    3. Deep neural network based speech understanding improvement
    4. Audio and speech compression using deep learning
    5. Deep learning in multi-speaker localization
    6. Deep learning in brain signal based speech perception assessment

    Relevant topics that would be considered for inclusion in this special issue include, but are not limited to:

  • Guidelines for Submission

    Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.

    Papers should be formatted according to the guidelines for authors (see: http://www.mathcomputer.org/submission). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.

  • Published Papers

    The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.

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