Mathematics and Signal Processing in Acoustics
Shristi Rajbamshi studied Communications and Multimedia Engineering at the University of Erlangen-Nürnberg in Erlangen, Germany. She graduated with the Master's thesis entitled "Binaural Dereveberation for Hearing Aids", in which she developed a system that used binaural cues to suppress reverberation for hearing aid users.
Since December 2017, she has been employed at Acoustic Research Institute as a doctoral student in the workgroup "Mathematics and Signal Processing in Acoustics", where she works on the FLAME project (Frames and Linear Operator for Acoustical Modeling and Parameter Estimation). She is working on frame multipliers, e.g., best approximation by gabor multipliers, etc.
- Tauböck G.; Rajbamshi (2020) Sparse Audio Inpainting: A Dictionary Learning Technique to Improve Its Performance. AES Show Fall 2020.
- Rajbamshi S.; Tauboeck G.; Balazs P.; Abreu L. D. (2020) Method for Signal Processing. .
- Tauböck G.; Rajbamshi (2020) Dictionary Learning for Sparse Audio Inpainting. AES Show Fall 2020.
- Tauböck G.; Rajbamshi S.; Balazs P.; Abreu D. (2019) Random Gabor Multipliers and Compressive Sensing. Proceedings of SampTA 2019.
- Rajbamshi S.; Balazs P.; Holighaus N. (2019) Adhoc method to Invert the Reassigned Time-Frequency Representation. Proceedings of the 23rd International Congress on Acoustics. S. 2789 - 2796.
- Rajbamshi S. (2019) Random Gabor Multipliers for Compressive Sensing: A Simulation Study. Proceedings of the EUSIPCO 2019.