Fake News in Social Media: Fake News Themes and Intentional Deception in the News and on Social Media
Book Chapter
Idrees, H., Dashtipour, K., Hussain, T., & Gogate, M. (2024)
Fake News in Social Media: Fake News Themes and Intentional Deception in the News and on Social Media. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (219-229). Springer. https://doi.org/10.1007/978-3-031-47590-0_11
From the start of the twenty-first century, online views and clicks have only increased. Within the last twenty years that has embedded through the use of social media. Within...
Audio-visual speech enhancement and separation by utilizing multi-modal self-supervised embeddings
Presentation / Conference Contribution
Chern, I.-C., Hung, K.-H., Chen, Y.-T., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J.-C. (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 sug...
Audio-visual speech enhancement and separation by leveraging multimodal self-supervised embeddings
Presentation / Conference Contribution
Chern, I.-C., Hung, K.-H., Chen, Y.-T., Hussain, T., Gogate, M., Hussain, A., Tsao, Y., & Hou, J.-C. (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 sug...
A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning
Presentation / Conference Contribution
Hussain, T., Diyan, M., Gogate, M., Dashtipour, K., Adeel, A., Tsao, Y., & Hussain, A. (2022, July)
A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning. Presented at 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology 麻豆社区 (EMBC), Glasgow, Scotland
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...
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., 鈥ussain, 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...
Towards intelligibility-oriented audio-visual speech enhancement
Presentation / Conference Contribution
Hussain, T., Gogate, M., Dashtipour, K., & Hussain, A. (2021, September)
Towards intelligibility-oriented audio-visual speech enhancement. Presented at The Clarity Workshop on Machine Learning Challenges for Hearing Aids (Clarity-2021), Online
Existing deep learning (DL) based approaches are generally optimised to minimise the distance between clean and enhanced speech features. These often result in improved speech...