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All Outputs (77)

Lip-reading driven deep learning approach for speech enhancement (2019)
Journal Article
Adeel, A., Gogate, M., Hussain, A., & Whitmer, W. M. (2021). Lip-reading driven deep learning approach for speech enhancement. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(3), 481-490. https://doi.org/10.1109/tetci.2019.2917039

This paper proposes a novel lip-reading driven deep learning framework for speech enhancement. The approach leverages the complementary strengths of both deep learning and analytical acoustic modeling (filtering-based approach) as compared to benchma... Read More about Lip-reading driven deep learning approach for speech enhancement.

Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments (2019)
Journal Article
Adeel, A., Gogate, M., & Hussain, A. (2020). Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments. Information Fusion, 59, 163-170. https://doi.org/10.1016/j.inffus.2019.08.008

Human speech processing is inherently multi-modal, where visual cues (e.g. lip movements) can help better understand speech in noise. Our recent work [1] has shown that lip-reading driven, audio-visual (AV) speech enhancement can significantly outper... Read More about Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments.

Deep Cognitive Neural Network (DCNN) (2019)
Patent
Howard, N., Adeel, A., Gogate, M., & Hussain, A. (2019). Deep Cognitive Neural Network (DCNN). US2019/0156189

Embodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), incorporating an architecture that integrates convolutional feedforward and... Read More about Deep Cognitive Neural Network (DCNN).

Cognitively inspired feature extraction and speech recognition for automated hearing loss testing (2019)
Journal Article
Nisar, S., Tariq, M., Adeel, A., Gogate, M., & Hussain, A. (2019). Cognitively inspired feature extraction and speech recognition for automated hearing loss testing. Cognitive Computation, 11(4), 489-502. https://doi.org/10.1007/s12559-018-9607-4

Hearing loss, a partial or total inability to hear, is one of the most commonly reported disabilities. A hearing test can be carried out by an audiologist to assess a patient’s auditory system. However, the procedure requires an appointment, which ca... Read More about Cognitively inspired feature extraction and speech recognition for automated hearing loss testing.

A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA) (2019)
Journal Article
Ozturk, M., Gogate, M., Onireti, O., Adeel, A., Hussain, A., & Imran, M. A. (2019). A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA). Neurocomputing, 358, 479-489. https://doi.org/10.1016/j.neucom.2019.01.031

One of the fundamental goals of mobile networks is to enable uninterrupted access to wireless services without compromising the expected quality of service (QoS). This paper reports a number of significant contributions. First, a novel analytical mod... Read More about A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA).

Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection (2018)
Presentation / Conference Contribution
Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K., Morabito, F., Larijani, H., Raza, A., & Hussain, A. (2018, July). Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection. Presented at 9th International Conference, BICS 2018, Xi'an, China

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology (ICT) systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially... Read More about Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection.

Exploiting Deep Learning for Persian Sentiment Analysis (2018)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Ieracitano, C., Larijani, H., & Hussain, A. (2018, July). Exploiting Deep Learning for Persian Sentiment Analysis. Presented at 9th International Conference, BICS 2018, Xi'an, China

The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspe... Read More about Exploiting Deep Learning for Persian Sentiment Analysis.

A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management (2018)
Book Chapter
Adeel, A., Gogate, M., Farooq, S., Ieracitano, C., Dashtipour, K., Larijani, H., & Hussain, A. (2019). A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management. In T. S. Durrani, W. Wang, & S. M. Forbes (Eds.), Geological Disaster Monitoring Based on Sensor Networks (57-66). Springer. https://doi.org/10.1007/978-981-13-0992-2_5

Extreme events and disasters resulting from climate change or other ecological factors are difficult to predict and manage. Current limitations of state-of-the-art approaches to disaster prediction and management could be addressed by adopting new un... Read More about A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management.

Toward's Arabic multi-modal sentiment analysis (2018)
Presentation / Conference Contribution
Alqarafi, A., Adeel, A., Gogate, M., Dashtipour, K., Hussain, A., & Durrani, T. (2017, July). Toward's Arabic multi-modal sentiment analysis. Presented at International Conference in Communications, Signal Processing, and Systems, Harbin, China

In everyday life, people use internet to express and share opinions, facts, and sentiments about products and services. In addition, social media applications such as Facebook, Twitter, WhatsApp, Snapchat etc., have become important information shari... Read More about Toward's Arabic multi-modal sentiment analysis.

A comparative study of Persian sentiment analysis based on different feature combinations (2018)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Hussain, A., Alqarafi, A., & Durrani, T. (2017, July). A comparative study of Persian sentiment analysis based on different feature combinations. Presented at International Conference in Communications, Signal Processing, and Systems, Harbin, China

In recent years, the use of internet and correspondingly the number of online reviews, comments and opinions have increased significantly. It is indeed very difficult for humans to read these opinions and classify them accurately. Consequently, there... Read More about A comparative study of Persian sentiment analysis based on different feature combinations.

Deep learning driven multimodal fusion for automated deception detection (2018)
Presentation / Conference Contribution
Gogate, M., Adeel, A., & Hussain, A. (2017, November). Deep learning driven multimodal fusion for automated deception detection. Presented at 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA

Humans ability to detect lies is no more accurate than chance according to the American Psychological Association. The state-of-the-art deception detection methods, such as deception detection stem from early theories and polygraph have proven to be... Read More about Deep learning driven multimodal fusion for automated deception detection.

A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition (2018)
Presentation / Conference Contribution
Gogate, M., Adeel, A., & Hussain, A. (2017, November). A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition. Presented at 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA

The curse of dimensionality is a well-established phenomenon. However, the properties of high dimensional data are often poorly understood and overlooked during the process of data modelling and analysis. Similarly, how to optimally fuse different mo... Read More about A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition.

Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo (2017)
Presentation / Conference Contribution
Adeel, A., Gogate, M., & Hussain, A. (2017, August). Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo. Presented at 1st International Workshop on Challenges in Hearing Assistive Technology (CHAT 2017), Stockholm, Sweden

Speech enhancement aims to enhance the perceived speech quality and intelligibility in the presence of noise. Classical speech enhancement methods are mainly based on audio only processing which often perform poorly in adverse conditions, where overw... Read More about Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo.

Complex-valued computational model of hippocampal CA3 recurrent collaterals (2017)
Presentation / Conference Contribution
Shiva, A., Gogate, M., Howard, N., Graham, B., & Hussain, A. (2017, July). Complex-valued computational model of hippocampal CA3 recurrent collaterals. Presented at 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK

Complex planes are known to simplify the complexity of real world problems, providing a better comprehension of their functionality and design. The need for complex numbers in both artificial and biological neural networks is equally well established... Read More about Complex-valued computational model of hippocampal CA3 recurrent collaterals.

Persian Named Entity Recognition (2017)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Algarafi, A., Howard, N., & Hussain, A. (2017, July). Persian Named Entity Recognition. Presented at 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK

Named Entity Recognition (NER) is an important natural language processing (NLP) tool for information extraction and retrieval from unstructured texts such as newspapers, blogs and emails. NER involves processing unstructured text for classification... Read More about Persian Named Entity Recognition.

A Generative Learning Approach to Sensor Fusion and Change Detection (2016)
Journal Article
Gepperth, A. R. T., Hecht, T., & Gogate, M. (2016). A Generative Learning Approach to Sensor Fusion and Change Detection. Cognitive Computation, 8(5), 806-817. https://doi.org/10.1007/s12559-016-9390-z

We present a system for performing multi-sensor fusion that learns from experience, i.e., from training data and propose that learning methods are the most appropriate approaches to real-world fusion problems, since they are largely model-free and th... Read More about A Generative Learning Approach to Sensor Fusion and Change Detection.