All Outputs (14)

Formulations and Algorithms to Find Maximal and Maximum Independent Sets of Graphs (2023)
Conference Proceeding
Heal, M., Dashtipour, K., & Gogate, M. (2023). Formulations and Algorithms to Find Maximal and Maximum Independent Sets of Graphs. In Proceedings, 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022. https://doi.org/10.1109/csci58124.2022.00097

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 multi-variable polynomial programming algorithms. Two other algorithms run i... Read More about Formulations and Algorithms to Find Maximal and Maximum Independent Sets of Graphs.

Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids (2023)
Conference Proceeding
Gogate, M., Dashtipour, K., & Hussain, A. (2023). Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). https://doi.org/10.1109/icasspw59220.2023.10192961

Classical audio-visual (AV) speech enhancement (SE) and separation methods have been successful at operating under constrained environments; however, the speech quality and intelligibility improvement is significantly reduced in unconstrained real-wo... Read More about Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids.

Towards Individualised Speech Enhancement: An SNR Preference Learning System for Multi-Modal Hearing Aids (2023)
Conference Proceeding
Kirton-Wingate, J., Ahmed, S., Gogate, M., Tsao, Y., & Hussain, A. (2023). Towards Individualised Speech Enhancement: An SNR Preference Learning System for Multi-Modal Hearing Aids. In K. Dashtipour (Ed.), Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). https://doi.org/10.1109/icasspw59220.2023.10193122

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 artefacts, sound unnatural, and restrict the ability for a user to hear am... Read More about Towards Individualised Speech Enhancement: An SNR Preference Learning System for Multi-Modal Hearing Aids.

Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype (2023)
Conference Proceeding
Gogate, M., Hussain, A., Dashtipour, K., & Hussain, A. (2023). Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype. In IEEE ISCAS 2023 Symposium Proceedings. https://doi.org/10.1109/iscas46773.2023.10182070

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 multifaceted but include ineffectiveness of current HA technology in noisy envir... Read More about Live Demonstration: Real-time Multi-modal Hearing Assistive Technology Prototype.

Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids (2023)
Conference Proceeding
Bishnu, A., Gupta, A., Gogate, M., Dashtipour, K., Arslan, T., Adeel, A., …Ratnarajah, T. (2023). Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids. In IEEE ISCAS 2023 Symposium Proceedings. https://doi.org/10.1109/iscas46773.2023.10182060

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 enhancement (SE) algorithms used in modern hearing aids face significant hurdles si... Read More about Live Demonstration: Cloud-based Audio-Visual Speech Enhancement in Multimodal Hearing-aids.

The P vs. NP Problem and Attempts to Settle It via Perfect Graphs State-of-the-Art Approach (2023)
Conference Proceeding
Heal, M., Dashtipour, K., & Gogate, M. (2023). The P vs. NP Problem and Attempts to Settle It via Perfect Graphs State-of-the-Art Approach. In Advances in Information and Communication: Proceedings of the 2023 Future of Information and Communication Conference (FICC), Volume 2 (328-340). https://doi.org/10.1007/978-3-031-28073-3_23

The P vs. NP problem is a major problem in computer science. It is perhaps the most celebrated outstanding problem in that domain. Its solution would have a tremendous impact on different fields such as mathematics, cryptography, algorithm research,... Read More about The P vs. NP Problem and Attempts to Settle It via Perfect Graphs State-of-the-Art Approach.

AVSE Challenge: Audio-Visual Speech Enhancement Challenge (2023)
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). https://doi.org/10.1109/slt54892.2023.10023284

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 intelligibility in adverse listening scenarios that are currently too chal... Read More about AVSE Challenge: Audio-Visual Speech Enhancement Challenge.

A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids (2022)
Conference Proceeding
Bishnu, A., Gupta, A., Gogate, M., Dashtipour, K., Adeel, A., Hussain, A., …Ratnarajah, T. (2022). A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids. In 2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom). https://doi.org/10.1109/healthcom54947.2022.9982772

In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology. The... Read More about A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids.

A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning (2022)
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 speech and enhanced speech signals. Despite improving the speech quality, su... Read More about A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning.

Comparing the Performance of Different Classifiers for Posture Detection (2022)
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 compares the performance of different classifiers in the detection of 3 post... Read More about Comparing the Performance of Different Classifiers for Posture Detection.

Detecting Alzheimer’s Disease Using Machine Learning Methods (2022)
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’s disease is expected to increase rapidly in the next few years. Currently,... Read More about Detecting Alzheimer’s Disease Using Machine Learning Methods.

Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System (2020)
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, recorded in real noisy environments including social gatherings, streets, cafeteri... Read More about Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System.

Deep Neural Network Driven Binaural Audio Visual Speech Separation (2020)
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 disambiguate phonological ambiguity. In this study, inspired from this uni... Read More about Deep Neural Network Driven Binaural Audio Visual Speech Separation.

Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances (2020)
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 broad-spectrum of applications rending from, bank cheque processing, applicati... Read More about Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances.