Accurate estimation of the noise power spectral density (PSD) is essential for effective single-channel speech enhancement in hearing aids, where stringent computational and latency constraints demand efficient noise estimation algorithms. This thesis investigates low-complexity noise PSD estimation techniques for system-on-chip (SoC)-based hearing aids, with a primary focus on minimum mean-square error (MMSE) estimation. After reviewing representative state-of-the-art signal-processing-based approaches, including minimum statistics, recursive averaging and MMSE-based estimators, an improved MMSE-based noise PSD estimation method is proposed to achieve a better trade-off between estimation accuracy, tracking capability, and computational complexity for resource-constrained hearing-aid platforms.

The proposed algorithm is evaluated under stationary and non-stationary noise conditions using synthetic, real, and speech-contaminated signals across multiple signal-to-noise ratios. Experimental results demonstrate improved noise tracking accuracy and reduced estimation variance while maintaining computational efficiency suitable for real-time embedded implementation. The proposed method provides an effective and practical solution for robust noise PSD estimation in next-generation hearing-aid systems.
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