MSc thesis project proposal
 Small-footprint Embedded Real-Time Speech Enhancement for Cochlea Implant
Cochlear implants (CI) [1, 2] are miniaturized biomedical devices that can help deaf people perceive sound or help hearing loss patients understand speech better. The CI has an in-vitro module attached behind the ear and an in-vivo implant surgically placed under the skin. The quality of CI output signals degrades in noisy environments and relies on Speech enhancement (SE) systems to enhance its performance. Neural network-based SEs  achieve state-of-the-art performance but are expensive to deploy on CI with a limited power budget. In this work, you will build a small-footprint NN-based SE system that can run on embedded devices [4, 5, 6] in real-time.
Hochmair, I., Nopp, P., Jolly, C., Schmidt, M., Schösser, H., Garnham, C., & Anderson, I. (2006). MED-EL Cochlear implants: state of the art and a glimpse into the future. Trends in amplification, 10(4), 201–219. https://doi.org/10.1177/1084713806296720
Joseph Tierny, Marc A. Zissman, and Donald K. Eddington. 1994. Digital signal processing applications in cochlear-implant research. <i>Lincoln Lab. J.</i> 7, 1 (Spring/Summer 1994), 31–62.
Nengheng Zheng, et al., “A noise-robust signal processing strategy for cochlear implants using neural networks”, ICASSP 2021.
- Survey the CI market to understand the performance requirements of SE and its latency & power constraints.
- Design a small-footprint neural network-based SE algorithm or optimize an existing SE system.
- Implement the SE in an embedded platform (μC or FPGA) with real-time operation capability. (CUDA/High-Level Synthesis/SystemVerilog)
- Experience with Python and PyTorch.
- Experience in using μC is a plus.
- Digital design with Verilog or Xilinx HLS (optional).
dr. Chang Gao
Electronic Circuits and Architectures Group
Department of Microelectronics
Last modified: 2023-03-22