All Outputs (6)

A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks (2019)
Journal Article
Dashtipour, K., Gogate, M., Li, J., Jiang, F., Kong, B., & Hussain, A. (2020). A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing, 380, 1-10. https://doi.org/10.1016/j.neucom.

Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language. Current sentiment... Read More about A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks.

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,

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).