All Outputs (7)

ASPIRE - Real noisy audio-visual speech enhancement corpus (2020)
Dataset
Gogate, M., Dashtipour, K., Adeel, A., & Hussain, A. (2020). ASPIRE - Real noisy audio-visual speech enhancement corpus. [Dataset]. https://doi.org/10.5281/zenodo.4585619

ASPIRE is a a first of its kind, audiovisual speech corpus recorded in real noisy environment (such as cafe, restaurants) which can be used to support reliable evaluation of multi-modal Speech Filtering technologies. This dataset follows the same sen... Read More about ASPIRE - Real noisy audio-visual speech enhancement corpus.

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.

Robust Visual Saliency Optimization Based on Bidirectional Markov Chains (2020)
Journal Article
Jiang, F., Kong, B., Li, J., Dashtipour, K., & Gogate, M. (2021). Robust Visual Saliency Optimization Based on Bidirectional Markov Chains. Cognitive Computation, 13, 69–80. https://doi.org/10.1007/s12559-020-09724-6

Saliency detection aims to automatically highlight the most important area in an image. Traditional saliency detection methods based on absorbing Markov chain only take into account boundary nodes and often lead to incorrect saliency detection when t... Read More about Robust Visual Saliency Optimization Based on Bidirectional Markov Chains.

CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement (2020)
Journal Article
Gogate, M., Dashtipour, K., Adeel, A., & Hussain, A. (2020). CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement. Information Fusion, 63, 273-285. https://doi.org/10.1016/j.inffus.2020.04.001

Noisy situations cause huge problems for the hearing-impaired, as hearing aids often make speech more audible but do not always restore intelligibility. In noisy settings, humans routinely exploit the audio-visual (AV) nature of speech to selectively... Read More about CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement.

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.

Random Features and Random Neurons for Brain-Inspired Big Data Analytics (2020)
Conference Proceeding
Gogate, M., Hussain, A., & Huang, K. (2020). Random Features and Random Neurons for Brain-Inspired Big Data Analytics. In 2019 International Conference on Data Mining Workshops (ICDMW). https://doi.org/10.1109/icdmw.2019.00080

With the explosion of Big Data, fast and frugal reasoning algorithms are increasingly needed to keep up with the size and the pace of user-generated contents on the Web. In many real-time applications, it is preferable to be able to process more data... Read More about Random Features and Random Neurons for Brain-Inspired Big Data Analytics.