Journal Information
Neural Networks
http://www.journals.elsevier.com/neural-networks/
Impact Factor:
5.785
Publisher:
ELSEVIER
ISSN:
0893-6080
Viewed:
10424
Tracked:
30

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Call For Papers
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.
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Advances in Deep Learning Based Speech Processing
Submission Date: 2020-03-30

Deep learning has triggered a big revolution inon 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. Such This tremendous success is achieved by the long-term evolutionadvances of neural network technologies as well as the big explosion of speech data and fast development of computing power. Although such a bigDespite this tremendous success has been made, 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 overis typically less than 50%; end-to-end deep learning based speech processing systems have shown potential advantages over hybrid systems, however, they still have a high requirement ofto large-scale labelled speech data; deep learning based speech synthesis has been highly competitive with human-sounding speech and much better than to traditional methods, however, the models are not stable, lacks controllability and are still too large and slow to be able to deployedput into mobile and IoT devices, etc. AccordinglyTherefore, new theoretical methods in deep learning and speech processing are required needed to tackle the above challenges, as well as to yield novel insights into new directions and applicationproblems. This special issue on recent advances of deep learning based speech processing invitesaims to accelerate research progress by providing a forum for researchers and practitioners to present novel their latest contributions that advance addressing theoretical and practical aspects of deep learning related speech processing techniques. The special issue will feature a collection of high quality theoretical articles with novel new insights and creative solutions to the key research challenges, as well asand state-of-the-art speech processing algorithms/systems that demonstrate highly-competitive performance with potential industrial impacts. The technologies addressing emerging problems and directions are also very welcome. 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
Last updated by Dou Sun in 2020-02-23
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