Ferdous Sohel

Dr, BSc B'desh.Engin., PhD Monash

  • The University of Western Australia (M002), 35 Stirling Highway,

    6009 Perth


  • 1612 Citations
  • 18 h-Index
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Fingerprint Dive into the research topics where Ferdous Sohel is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 3 Similar Profiles
Acoustics Engineering & Materials Science
Object recognition Engineering & Materials Science
Fusion reactions Engineering & Materials Science
Neural networks Engineering & Materials Science
Labeling Engineering & Materials Science
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Biometrics Engineering & Materials Science
Reefs Engineering & Materials Science

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Research Output 2004 2019

Acoustic Scene Classification Using Joint Time-Frequency Image-Based Feature Representations

Abidin, S., Togneri, R. & Sohel, F., 11 Feb 2019, Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance. IEEE, Institute of Electrical and Electronics Engineers, 8639164. (Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance).

Research output: Chapter in Book/Conference paperConference paper

Fusion reactions
Mathematical transformations

A Survey: Neural Network-Based Deep Learning for Acoustic Event Detection

Xia, X., Togneri, R., Sohel, F., Zhao, Y. & Huang, D., Aug 2019, In : Circuits, Systems, and Signal Processing. 38, 8, p. 3433-3453

Research output: Contribution to journalArticle

Event Detection
Neural Networks
Neural networks
Speech Processing

Deep learning for scene understanding

Nadeem, U., Shah, S. A. A., Sohel, F., Togneri, R. & Bennamoun, M., 19 Mar 2019, Handbook of deep learning applications. Balas, V. E., Roy, S. S., Sharma, D. & Samui, P. (eds.). Cham: Springer, p. 21-51 27 p. (Smart Innovation, Systems and Technologies; vol. 136).

Research output: Chapter in Book/Conference paperChapter

Computer vision
Neural networks
Network architecture
Deep learning
Open Access
artificial intelligence
heart failure
Learning systems
Feature extraction
Heart Failure

Global regularizer and temporal-aware cross-entropy for skeleton-based early action recognition

Ke, Q., Liu, J., Bennamoun, M., Rahmani, H., An, S., Sohel, F. & Boussaid, F., 2019, Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers. Mori, G., Li, H., Schindler, K. & Jawahar, C. V. (eds.). Springer-Verlag Berlin, p. 729-745 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11364 LNCS).

Research output: Chapter in Book/Conference paperConference paper

Action Recognition

Projects 2008 2015