Mandar Gogate
mandar gogate

Dr. Mandar Gogate

Senior Research Fellow

 0131 455 4793

  M.Gogate@napier.ac.uk

   

Biography

Dr Mandar Gogate is an EPSRC Research Fellow at Edinburgh Napier University’s School of Computing specialising in real-time speech enhancement, multimodal signal processing, and machine learning. Mandar graduated with a B.Eng (highest 1st Class Honours with distinction) in Electrical and Electronic Engineering at India’s top Birla Institute of Technology & Science, Pilani. He was awarded a PhD degree in Computing Science by Edinburgh Napier University in 2020.

Previously, he worked as an invited Research Scientist at Amazon and ENSTA ParisTech - École Nationale Supérieure de Techniques Avancées, Paris, France where he researched multimodal robotic sensor fusion technologies, incremental learning and dynamic advertising. He has also been an invited visiting research fellow at Sonova AG (Switzerland), MIT (Synthetic Intelligence Lab), and University of Oxford (Computational Neuroscience Lab).

His research interests are interdisciplinary, and include: real-time audio-visual enhancement, multimodal fusion, incremental learning, natural language processing, computer vision, sentiment and emotion analysis, privacy-preserving machine learning, explainable and ‘human-in-the-loop’ artificial intelligence, IoT, wireless sensing and 5G communications. Real-world applications range from cognitive robotics and assistive healthcare technologies to ​​automated social media analytics for security, business intelligence and industry 4.0.

School

Research Groups

Research Areas

Esteem

Conference Organising Activity

  • Interspeech 2024 Satellite Workshop - 3rd COG-MHEAR Audio-Visual Speech Enhancement Challenge (AVSEC-3)

 

Spin-outs and Licences

  • Patent: Deep Cognitive Neural Network (DCNN)

 

Visiting Positions

  • Academia Sinica, Taipei, Taiwan

 

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51 results

Deep Neural Network Driven Binaural Audio Visual Speech Separation

Conference Proceeding
Gogate, M., Dashtipour, K., Bell, P., & Hussain, A. (2020)
Deep Neural Network Driven Binaural Audio Visual Speech Separation. In 2020 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn48605.2020.9207517
The central auditory pathway exploits the auditory signals and visual information sent by both ears and eyes to segregate speech from multiple competing noise sources and help...

Robust Visual Saliency Optimization Based on Bidirectional Markov Chains

Journal Article
Jiang, F., Kong, B., Li, J., Dashtipour, K., & Gogate, M. (2021)
Robust Visual Saliency Optimization Based on Bidirectional Markov Chains. Cognitive Computation, 13, 69–80. https://doi.org/10.1007/s12559-020-09724-6
Saliency detection aims to automatically highlight the most important area in an image. Traditional saliency detection methods based on absorbing Markov chain only take into a...

CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement

Journal Article
Gogate, M., Dashtipour, K., Adeel, A., & Hussain, A. (2020)
CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement. Information Fusion, 63, 273-285. https://doi.org/10.1016/j.inffus.2020.04.001
Noisy situations cause huge problems for the hearing-impaired, as hearing aids often make speech more audible but do not always restore intelligibility. In noisy settings, hum...

Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances

Conference Proceeding
Ahmed, R., Dashtipour, K., Gogate, M., Raza, A., Zhang, R., Huang, K., …Hussain, A. (2020)
Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances. In Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings (457-468). https://doi.org/10.1007/978-3-030-39431-8_44
In pattern recognition, automatic handwriting recognition (AHWR) is an area of research that has developed rapidly in the last few years. It can play a significant role in bro...

Random Features and Random Neurons for Brain-Inspired Big Data Analytics

Conference Proceeding
Gogate, M., Hussain, A., & Huang, K. (2020)
Random Features and Random Neurons for Brain-Inspired Big Data Analytics. In 2019 International Conference on Data Mining Workshops (ICDMW). https://doi.org/10.1109/icdmw.2019.00080
With the explosion of Big Data, fast and frugal reasoning algorithms are increasingly needed to keep up with the size and the pace of user-generated contents on the Web. In ma...

A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

Journal Article
Dashtipour, K., Gogate, M., Li, J., Jiang, F., Kong, B., & Hussain, A. (2020)
A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing, 380, 1-10. https://doi.org/10.1016/j.neucom.2019.10.009
Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, how...

Lip-Reading Driven Deep Learning Approach for Speech Enhancement

Journal Article
Adeel, A., Gogate, M., Hussain, A., & Whitmer, W. M. (2021)
Lip-Reading Driven Deep Learning Approach for Speech Enhancement. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(3), 481-490. https://doi.org/10.1109/tetci.2019.2917039
This paper proposes a novel lip-reading driven deep learning framework for speech enhancement. The approach leverages the complementary strengths of both deep learning and ana...

Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments

Journal Article
Adeel, A., Gogate, M., & Hussain, A. (2020)
Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments. Information Fusion, 59, 163-170. https://doi.org/10.1016/j.inffus.2019.08.008
Human speech processing is inherently multi-modal, where visual cues (e.g. lip movements) can help better understand speech in noise. Our recent work [1] has shown that lip-re...

Deep Cognitive Neural Network (DCNN)

Patent
Howard, N., Adeel, A., Gogate, M., & Hussain, A. (2019)
Deep Cognitive Neural Network (DCNN). US2019/0156189
Embodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), ...

Cognitively Inspired Feature Extraction and Speech Recognition for Automated Hearing Loss Testing

Journal Article
Nisar, S., Tariq, M., Adeel, A., Gogate, M., & Hussain, A. (2019)
Cognitively Inspired Feature Extraction and Speech Recognition for Automated Hearing Loss Testing. Cognitive Computation, 11(4), 489-502. https://doi.org/10.1007/s12559-018-9607-4
Hearing loss, a partial or total inability to hear, is one of the most commonly reported disabilities. A hearing test can be carried out by an audiologist to assess a patient’...

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