Mandar Gogate
mandar gogate

Dr. Mandar Gogate

Senior Research Fellow

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.

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

 

Date


51 results

Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps

Journal Article
Li, J., Jiang, F., Yang, J., Kong, B., Gogate, M., Dashtipour, K., & Hussain, A. (2021)
Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps. Neurocomputing, 465, 15-25. https://doi.org/10.1016/j.neucom.2021.08.105
Accurate high-definition maps with lane markings are often used as the navigation back-end for commercial autonomous vehicles. Currently, most high-definition maps are manuall...

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

Journal Article
Dashtipour, K., Taylor, W., Ansari, S., Gogate, M., Zahid, A., Sambo, Y., …Imran, M. A. (2021)
Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media. Frontiers in Big Data, 4, https://doi.org/10.3389/fdata.2021.640868
With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these t...

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Journal Article
Dashtipour, W., Gogate, M., Adeel, A., Larijani, H., & Hussain, A. (2021)
Sentiment Analysis of Persian Movie Reviews Using Deep Learning. Entropy, 23(5), https://doi.org/10.3390/e23050596
Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, n...

Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

Journal Article
Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., …Sheikh, A. (in press)
Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study. Journal of Medical Internet Research, 23(4), https://doi.org/10.2196/26627
Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccin...

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Journal Article
Ahmed, R., Gogate, M., Tahir, A., Dashtipour, K., Al-tamimi, B., Hawalah, A., …Hussain, A. (2021)
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts. Entropy, 23(3), https://doi.org/10.3390/e23030340
Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several field...

A novel context-aware multimodal framework for persian sentiment analysis

Journal Article
Dashtipour, K., Gogate, M., Cambria, E., & Hussain, A. (2021)
A novel context-aware multimodal framework for persian sentiment analysis. Neurocomputing, 457, 377-388. https://doi.org/10.1016/j.neucom.2021.02.020
Most recent works on sentiment analysis have exploited the text modality. However, millions of hours of video recordings posted on social media platforms everyday hold vital u...

A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect

Journal Article
Guellil, I., Adeel, A., Azouaou, F., Benali, F., Hachani, A., Dashtipour, K., …Hussain, A. (2021)
A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect. SN Computer Science, 2, Article 118. https://doi.org/10.1007/s42979-021-00510-1
In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically...

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Journal Article
Churcher, A., Ullah, R., Ahmad, J., Ur Rehman, S., Masood, F., Gogate, M., …Buchanan, W. J. (2021)
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks. Sensors, 21(2), Article 446. https://doi.org/10.3390/s21020446
In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices i...

ASPIRE - Real noisy audio-visual speech enhancement corpus

Dataset
Gogate, M., Dashtipour, K., Adeel, A., & Hussain, A. (2020)
ASPIRE - Real noisy audio-visual speech enhancement corpus. [Dataset]. https://doi.org/10.5281/zenodo.4585619
ASPIRE is a a first of its kind, audiovisual speech corpus recorded in real noisy environment (such as cafe, restaurants) which can be used to support reliable evaluation of m...

Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System

Conference Proceeding
Gogate, M., Dashtipour, K., & Hussain, A. (2020)
Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System. In Proc. Interspeech 2020 (4521-4525). https://doi.org/10.21437/interspeech.2020-2935
In this paper, we present VIsual Speech In real nOisy eNvironments (VISION), a first of its kind audio-visual (AV) corpus comprising 2500 utterances from 209 speakers, recorde...

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