A Hybrid FPGA and Machine Learning Framework for Enhancing Hearing Aid Performance

An FPGA-based digital hearing aid system will enhance the living quality of patients with hearing loss. In this paper, a design and implementation of a digital hearing aid system are discussed, based on FPGA technology, by integrating **machine learning (ML) algorithms** for adaptive scene classification and noise reduction, alongside amplification and DAC conversion, to produce a clearer, enhanced audio signal. The algorithm of ML does classification of the acoustic environment dynamically; examples include quiet, noisy, and speech-dominant classes. Noise reduction filters adapt to every such class for optimal speech clarity. The design methodology to be followed is modular: separate sub-modules for the ADC, ML-based noise reduction, amplification, and DAC processes. This would keep the system scalable, simpler, and easier to debug. The simulation results and the ML-based analysis confirm that the system is effective in both processing and enhancing audio signals through different environments. This research presents a cost-effective, intelligent, and efficient real-time solution to address auditory challenges.