Mandar Gogate
mandar gogate

Dr. Mandar Gogate

Senior Research Fellow


Dr Mandar Gogate is an EPSRC Senior Research Fellow at Edinburgh Napier University’s ​School of Computing, Engineering & built Environment specialising in real-time multimodal speech enhancement, signal processing, machine learning and artificial intelligence. 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), Academia Sinica (Taiwan), MIT (Synthetic Intelligence Lab), and University of Oxford (Computational Neuroscience Lab).

His research interests are interdisciplinary, and include: real-time audio-visual enhancement, multimodal sensor fusion, incremental learning, natural language processing, computer vision, sentiment and emotion analysis, privacy-preserving machine learning, explainable 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.

Research Areas


Conference Organising Activity

  • 2024 Interspeech Conference: International Satellite Workshop on the 3rd COG-MHEAR Audio-Visual Speech Enhancement Challenge, 1 Sep 2024, Greece


Spin-outs and Licences

  • Patent: Deep Cognitive Neural Network (DCNN)



52 results

Impact of the Covid-19 pandemic on audiology service delivery: Observational study of the role of social media in patient communication

Journal Article
The Covid-19 pandemic has highlighted an era in hearing health care that necessitates a comprehensive rethinking of audiology service delivery. There has been a significant in...

Robust Real-time Audio-Visual Speech Enhancement based on DNN and GAN

Journal Article
The human auditory cortex contextually integrates audio-visual (AV) cues to better understand speech in a cocktail party situation. Recent studies have shown that AV speech en...

A hybrid dependency-based approach for Urdu sentiment analysis

Journal Article
In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse...

Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids

Conference Proceeding
Classical audio-visual (AV) speech enhancement (SE) and separation methods have been successful at operating under constrained environments; however, the speech quality and in...

Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis

Journal Article
Sentiment analysis can be used to derive knowledge that is connected to emotions and opinions from textual data generated by people. As computer power has grown, and the avail...

Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids

Conference Proceeding
Since the advent of deep learning (DL), speech enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic...

Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype

Conference Proceeding
Hearing loss affects at least 1.5 billion people globally. The WHO estimates 83% of people who could benefit from hearing aids do not use them. Barriers to HA uptake are multi...

Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids

Conference Proceeding
Hearing loss is among the most serious public health problems, affecting as much as 20% of the worldwide population. Even cutting-edge multi-channel audio-only speech enhancem...

AVSE Challenge: Audio-Visual Speech Enhancement Challenge

Conference Proceeding
Aldana Blanco, A. L., Valentini-Botinhao, C., Klejch, O., Gogate, M., Dashtipour, K., Hussain, A., & Bell, P. (2023)
AVSE Challenge: Audio-Visual Speech Enhancement Challenge. In 2022 IEEE Spoken Language Technology Workshop (SLT) (465-471).
Audio-visual speech enhancement is the task of improving the quality of a speech signal when video of the speaker is available. It opens-up the opportunity of improving speech...

Formulations and Algorithms to Find Maximal and Maximum Independent Sets of Graphs

Conference Proceeding
We propose four algorithms to find maximal and maximum independent sets of graphs. Two of the algorithms are non-polynomial in time, mainly binary programming and non-convex m...

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