Outputs (173)

Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System (2024)
Presentation / Conference Contribution
Gupta, A., Bishnu, A., Ratnarajah, T., Adeel, A., Hussain, A., & Sellathurai, M. (2023, December). Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System. Presented at GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia

Designing an efficient receiver for multiple users transmitting orthogonal frequency-division multiplexing signals to the base station remain a challenging interference-limited problem in 5G-new radio (5G-NR) system. This can lead to stagnation of de... Read More about Deep Learning-Based Receiver Design for IoT Multi-User Uplink 5G-NR System.

5G-IoT Cloud based Demonstration of Real-Time Audio-Visual Speech Enhancement for Multimodal Hearing-aids (2023)
Presentation / Conference Contribution
Gupta, A., Bishnu, A., Gogate, M., Dashtipour, K., Arslan, T., Adeel, A., Hussain, A., Ratnarajah, T., & Sellathurai, M. (2023, August). 5G-IoT Cloud based Demonstration of Real-Time Audio-Visual Speech Enhancement for Multimodal Hearing-aids. Presented at Interspeech 2023, Dublin, Ireland

Over twenty percent of the world's population suffers from some form of hearing loss, making it one of the most significant public health challenges. Current hearing aids commonly amplify noises while failing to improve speech comprehension in crowde... Read More about 5G-IoT Cloud based Demonstration of Real-Time Audio-Visual Speech Enhancement for Multimodal Hearing-aids.

Application for Real-time Audio-Visual Speech Enhancement (2023)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2023, August). Application for Real-time Audio-Visual Speech Enhancement. Presented at Interspeech 2023, Dublin, Ireland

This short paper demonstrates a first of its kind audio-visual (AV) speech enhancement (SE) desktop application that isolates, in real-time, the voice of a target speaker from noisy audio input. The deep neural network model integrated in this applic... Read More about Application for Real-time Audio-Visual Speech Enhancement.

Solving the cocktail party problem using Multi-modal Hearing Assistive Technology Prototype (2023)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2023, December). Solving the cocktail party problem using Multi-modal Hearing Assistive Technology Prototype. Presented at Acoustics 2023, Sydney, Australia

Hearing loss is a major global health problem, affecting over 1.5 billion people. According to estimations by the World Health Organization, 83% of those who could benefit from hearing assistive devices do not use them. The limited adoption of hearin... Read More about Solving the cocktail party problem using Multi-modal Hearing Assistive Technology Prototype.

Resolving the Decreased Rank Attack in RPL’s IoT Networks (2023)
Presentation / Conference Contribution
Ghaleb, B., Al-Duba, A., Hussain, A., Romdhani, I., & Jaroucheh, Z. (2023, June). Resolving the Decreased Rank Attack in RPL’s IoT Networks. Presented at 19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2023), Pafos, Cyprus

The Routing Protocol for Low power and Lossy networks (RPL) has been developed by the Internet Engineering Task Force (IETF) standardization body to serve as a part of the 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks) standard, a core... Read More about Resolving the Decreased Rank Attack in RPL’s IoT Networks.

Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings (2023)
Presentation / Conference Contribution
Chern, I., Hung, K., Chen, Y., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J. (2023, June). Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests that useful audio-visual speech representations can be obtained via u... Read More about Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings.

Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings (2023)
Presentation / Conference Contribution
Chern, I., Hung, K., Chen, Y., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J. (2023, June). Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests that useful audio-visual speech representations can be obtained via u... Read More about Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings.

Frequency-Domain Functional Links For Nonlinear Feedback Cancellation In Hearing Aids (2023)
Presentation / Conference Contribution
Nezamdoust, A., Gogate, M., Dashtipour, K., Hussain, A., & Comminiello, D. (2023, June). Frequency-Domain Functional Links For Nonlinear Feedback Cancellation In Hearing Aids. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

The problem of feedback cancellation can be seen as a function approximation task, which often is nonlinear in real-world hearing assistive technologies. Nonlinear methods adopted for this task must exhibit outstanding modeling performance and reduce... Read More about Frequency-Domain Functional Links For Nonlinear Feedback Cancellation In Hearing Aids.

Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids (2023)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2023, June). Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

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)
Presentation / Conference Contribution
Kirton-Wingate, J., Ahmed, S., Gogate, M., Tsao, Y., & Hussain, A. (2023, June). Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

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.