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Home / Journals / International Journal of Wireless Communications and Mobile Computing / Advanced Deep Learning Methods for Audio and Speech Processing
Advanced Deep Learning Methods for Audio and Speech Processing
Lead Guest Editor:
Peyman Goli
Khavaran Institute of Higher Education, Mashhad, Iran
Guest Editors
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
Professor 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
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