期刊信息
Neural Networks
http://www.journals.elsevier.com/neural-networks/
影响因子:
5.785
出版商:
Elsevier
ISSN:
0893-6080
浏览:
10857
关注:
30

征稿
Neural Networks is the archival journal of the world's three oldest neural modeling societies: the International Neural Network Society (INNS), the European Neural Network Society (ENNS), and the Japanese Neural Network Society (JNNS). A subscription to the journal is included with membership in each of these societies.

Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks and related approaches to computational intelligence. Neural Networks welcomes high quality submissions that contribute to the full range of neural networks research, from behavioral and brain modeling, learning algorithms, through mathematical and computational analyses, to engineering and technological applications of systems that significantly use neural network concepts and techniques. This uniquely broad range facilitates the cross-fertilization of ideas between biological and technological studies, and helps to foster the development of the interdisciplinary community that is interested in biologically-inspired computational intelligence. Accordingly, Neural Networks editorial board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics. The journal publishes articles, letters, reviews, and current opinions, as well as letters to the editor, book reviews, editorials, current events, software surveys, and patent information. Articles are published in one of five sections: Cognitive Science, Neuroscience, Learning Systems, Mathematical and Computational Analysis, Engineering and Applications.
最后更新 Dou Sun 在 2019-11-24
Special Issues
Special Issue on Advances in Deep Learning Based Speech Processing
截稿日期: 2020-06-30

Deep learning has triggered a revolution in speech processing. The revolution started from the successful application of deep neural networks to automatic speech recognition, and was quickly spread to other topics of speech processing, including speech analysis, speech denoising and separation, speaker and language recognition, speech synthesis, and spoken language understanding. This tremendous success is achieved by the advances of neural network technologies as well as the explosion of speech data and fast development of computing power. Despite this success, deep learning based speech processing still has many challenges for real-world wide deployment. For example, when the distance between a speaker and a microphone array is larger than 10 meters, the word error rate of a speech recognizer may be as high as over 50%; end-to-end deep learning based speech processing systems have shown potential advantages over hybrid systems, however, they require large-scale labelled speech data; deep learning based speech synthesis has been highly competitive with human-sounding speech and much better than traditional methods, however, the models are not stable, lack controllability and are still too large and slow to be deployed into mobile and IoT devices. Therefore, new methods and algorithms in deep learning and speech processing are needed to tackle the above challenges, as well as to yield novel insights into new directions and application. This special issue aims to accelerate research progress by providing a forum for researchers and practitioners to present their latest contributions that advance theoretical and practical aspects of deep learning based speech processing techniques. The special issue will feature theoretical articles with novel new insights, creative solutions to key research challenges, and state-of-the-art speech processing algorithms/systems that demonstrate competitive performance with potential industrial impacts. The ideas addressing emerging problems and directions are also welcome. Main Topics: Topics of interest for this special issue include, but are not limited to: Speaker separation Speech denoising Speech recognition Speaker and language recognition Speech synthesis Audio and speech analysis Multimodal speech processing Submission Procedure: Prospective authors should follow the standard author instructions for Neural Networks, and submit manuscripts online at http://ees.elsevier.com/neunet/. Authors should select “Speech Based on DL" when they reach the "Article Type" step and the "Request Editor" step in the submission process. Important Dates: June 30, 2020 - Submission deadline September 30, 2020 - First decision notification November 30, 2020 - Revised version deadline December 31, 2020 - Final decision notification March, 2021 - Publication Guest Editors: Xiao-Lei Zhang Northwestern Polytechnical University, China Email: xiaolei.zhang@nwpu.edu.cn Lei Xie Northwestern Polytechnical University, China Email: lxie@nwpu.edu.cn Eric Fosler-Lussier Ohio State University, USA Email: fosler-lussier.1@osu.edu Emmanuel Vincent Inria Nancy - Grand Est, France Email: emmanuel.vincent@inria.fr
最后更新 Dou Sun 在 2020-04-15
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相关会议
CCFCOREQUALIS简称全称截稿日期通知日期会议日期
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