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

Towards real-time privacy-preserving audio-visual speech enhancement

Presentation / Conference
Gogate, M., Dashtipour, K., & Hussain, A. (2022, September)
Towards real-time privacy-preserving audio-visual speech enhancement. Paper presented at 2nd Symposium on Security and Privacy in Speech Communication, Incheon, Korea
Human auditory cortex in everyday noisy situations is known to exploit aural and visual cues that are contextually combined by the brain’s multi-level integration strategies t...

A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning

Conference Proceeding
Hussain, T., Diyan, M., Gogate, M., Dashtipour, K., Adeel, A., Tsao, Y., & Hussain, A. (2022)
A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://doi.org/10.1109/embc48229.2022.9871113
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free s...

Arabic sentiment analysis using dependency-based rules and deep neural networks

Journal Article
Diwali, A., Dashtipour, K., Saeedi, K., Gogate, M., Cambria, E., & Hussain, A. (2022)
Arabic sentiment analysis using dependency-based rules and deep neural networks. Applied Soft Computing, 127, Article 109377. https://doi.org/10.1016/j.asoc.2022.109377
With the growth of social platforms in recent years and the rapid increase in the means of communication through these platforms, a significant amount of textual data is avail...

Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study

Journal Article
Hussain, Z., Sheikh, Z., Tahir, A., Dashtipour, K., Gogate, M., Sheikh, A., & Hussain, A. (2022)
Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study. JMIR Public Health and Surveillance, 8(5), Article e32543. https://doi.org/10.2196/32543
Background: The roll-out of vaccines for SARS-CoV-2 in the United Kingdom, started in December 2020. Uptake has been high, and there has been a subsequent reduction in infecti...

A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement

Journal Article
Hussain, T., Wang, W., Gogate, M., Dashtipour, K., Tsao, Y., Lu, X., …Hussain, A. (2022)
A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement. IEEE Transactions on Artificial Intelligence, 3(5), 833-842. https://doi.org/10.1109/TAI.2022.3169995
Removing background noise from acoustic observations to obtain clean signals is an important research topic regarding numerous real acoustic applications. Owing to their stron...

Comparing the Performance of Different Classifiers for Posture Detection

Conference Proceeding
Suresh Kumar, S., Dashtipour, K., Gogate, M., Ahmad, J., Assaleh, K., Arshad, K., …Ahmad, W. (2022)
Comparing the Performance of Different Classifiers for Posture Detection. In Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2021 (210-218). https://doi.org/10.1007/978-3-030-95593-9_17
Human Posture Classification (HPC) is used in many fields such as human computer interfacing, security surveillance, rehabilitation, remote monitoring, and so on. This paper c...

Detecting Alzheimer’s Disease Using Machine Learning Methods

Conference Proceeding
Dashtipour, K., Taylor, W., Ansari, S., Zahid, A., Gogate, M., Ahmad, J., …Abbasi, Q. (2022)
Detecting Alzheimer’s Disease Using Machine Learning Methods. In Body Area Networks. Smart IoT and Big Data for Intelligent Health Management 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings. https://doi.org/10.1007/978-3-030-95593-9_8
As the world is experiencing population growth, the portion of the older people, aged 65 and above, is also growing at a faster rate. As a result, the dementia with Alzheimer’...

COVID‐opt‐aiNet: A clinical decision support system for COVID‐19 detection

Journal Article
Kanwal, S., Khan, F., Alamri, S., Dashtipur, K., & Gogate, M. (2022)
COVID‐opt‐aiNet: A clinical decision support system for COVID‐19 detection. International Journal of Imaging Systems and Technology, 32(2), 444-461. https://doi.org/10.1002/ima.22695
Coronavirus disease (COVID-19) has had a major and sometimes lethal effect on global public health. COVID-19 detection is a difficult task that necessitates the use of intelli...

Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis

Journal Article
Dashtipour, K., Gogate, M., Gelbukh, A., & Hussain, A. (2021)
Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis. Social Network Analysis and Mining, 12(1), Article 9. https://doi.org/10.1007/s13278-021-00840-1
Nowadays, it is important for buyers to know other customer opinions to make informed decisions on buying a product or service. In addition, companies and organizations can ex...

Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Journal Article
Nawaz, H., Tahir, A., Ahmed, N., Fayyaz, U. U., Mahmood, T., Jaleel, A., …Abbasi, Q. (2021)
Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models. Entropy, 23(11), Article 1401. https://doi.org/10.3390/e23111401
Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor p...

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