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Signal Enhancement via Generative Models
This research is a cooperation between ISS and Apple Technology Services. My research is focused about using generative models in signal enhancement. More specifically genrative adversarial networks (GANs) operating on signals in different domains either raw time domain signals or in time-frequency (TF) representation or combination of both with main interest in:
- Speech enhancement using GANs: in a realistic scenario, an audio signal is a mixture between a desired signal and an unwanted interference or background noise. Audio enhancement can be analyzed as a source separation problem between the desired audio signal and the unwanted intrusive effect. For instance, speech denoising (eliminating background noise from the desired speech) is a vital building block for automatic speech recognition (ASR) systems, hearing aids and teleconferencing systems. Some common background noise types (cafe or food court) can be very similar to the desired speech. In these cases, estimating the desired speech from the corrupted signal is challenging and sometimes impossible in low SNR situations.
My research is focused on introducing new generative models for denoising of speech signal by operating on raw time domain or TF representations of noisy speech inputs.
- Radar signal enhancement and super-resolution: radar sensors are widely used nowadays in the tracking and identification of different targets. This is due to its superior penetration capabilities and robustness against different lighting and weather conditions in comparison to vision-based approaches such as camera and LIDAR. Additionally, radar is nowadays the dominant sensor used for autonomous driving as it combines high resolution in range, velocity and depth perception. This motivated the use of radar in vital tasks such as on-road pedestrians and cyclists recognition. However, most of the research assumes perfect measurement conditions where an ideal obstacle-free environment is preserved. Such requirements are not always applicable, and in many realistic use-cases. Moreover, radar suffers from stringent constraints on its design parametrization leading to multiple trade-offs in radar resolution.
My research is focused on using GANs for denoising and super-resolution of radar signals in TF representation.
Radar Micro-Doppler for Gait Recognition, Safety and Surveillance
This research is a cooperation between ISS and Fraunhofer IPA. Micro-Doppler (µ-D) spectrum introduces an additional capability in radar systems by allowing the analysis of the individual micro motions of moving targets. For instance, when studying the µ-D signature of a non-rigid body such as human motion, some frequencies reflect the translational velocity of the human and other frequencies reflect the swinging of different body limbs.
My research is focused on using different learning-based techniques operating on human µ-D signature for:
- Gait analysis and limbs decomposition
- Identifying and tracking humans from different moving targets
- Identification and recognition of different human subjects based on their respective motion signature
- S. Abdulatif, K. Armanious, K. Guirguis, J. T. Sajeev and B. Yang, "AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks," Submittted to 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Aug. 2020.
- S. Abdulatif, K. Armanious, A. R. Bhaktharaguttu, T. Küstner, B. Yang and S. Gatidis, "Organ-based estimation of the chronological age based on 3D MRI scans," 28th Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), Paris, Aug. 2020.
- S. Abdulatif, K. Armanious, F. Aziz, U. Schneider and B. Yang, "Towards Adversarial Denoising of Radar Micro-Doppler Signatures," IEEE International Radar Conference, Toulon, Sept. 2019.
- S. Abdulatif, F. Aziz, K. Armanious, B. Kleiner, B. Yang and U. Schneider, "Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers," IEEE Radar Conference (RadarConf), Boston, April 2019.
- S. Abdulatif, Q. Wei, F. Aziz, B. Kleiner and U. Schneider, "Micro-doppler based human-robot classification using ensemble and deep learning approaches," IEEE Radar Conference (RadarConf18), Oklahoma City, April 2018, pp. 1043-1048.
- S. Abdulatif, B. Kleiner, F. Aziz, C. Riehs, R. Cooper and U. Schneider, "Stairs detection for enhancing wheelchair capabilities based on radar sensors," IEEE 6th Global Conference on Consumer Electronics (GCCE), Nagoya, Oct. 2017.
- S. Abdulatif, F. Aziz, B. Kleiner and U. Schneider, "Real-time capable micro-Doppler signature decomposition of walking human limbs," IEEE Radar Conference (RadarConf), Seattle, April 2017, pp. 1093-1098.
- K. Armanious, S. Abdulatif, A. R. Bhaktharaguttu, T. Küstner, T. Hepp, S. Gatidis and B. Yang, "Organ-based Chronological Age Estimation based on 3D MRI Scans," Submittted to 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Aug. 2020.
- K. Guirguis, C. Schorn, A. Guntoro, S. Abdulatif and B. Yang, "SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks," Submittted to 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Aug. 2020.
- K. Armanious, V. Kumar, S. Abdulatif, T. Hepp, S. Gatidis and B. Yang, "ipA-MedGAN: Inpainting of Arbitrary Regions in Medical Imaging," 27th IEEE International Conference on Image Processing (ICIP), Abu Dhabi, Oct. 2020.
- K. Armanious, A. Tanwar, S. Abdulatif, T. Küstner, S. Gatidis and Bin Yang, "Unsupervised Adversarial Correction of Rigid MR Motion Artifacts," 17th IEEE International Symposium on Biomedical Imaging, Lowa City, April 2020.
- K. Armanious, S. Abdulatif, F. Aziz, U. Schneider and B. Yang, "An Adversarial Super-Resolution Remedy for Radar Design Trade-offs," 27th European Signal Processing Conference (EUSIPCO), A Coruna, Sept. 2019.
- K. Armanious, C. Jiang, S. Abdulatif, T. Küstner, S. Gatidis and B. Yang, "Unsupervised Medical Image Translation Using Cycle-MedGAN," 27th European Signal Processing Conference (EUSIPCO), A Coruna, Sept. 2019.
- M. Ulrich, T. Hess, S. Abdulatif and B. Yang, "Person Recognition Based on Micro-Doppler and Thermal Infrared Camera Fusion for Firefighting," 21st International Conference on Information Fusion (FUSION), Cambridge, July 2018, pp. 919-926.