Development of a Signal Processing Algorithm for Feedback (Noise) Reduction in Hearing Aids Using the Spectral Subtraction Technique

Despite numerous researches worldwide, feedback issue in hearing aids remains a challenge requiring further improvement. Existing methods employed to reduce feedback can at times be limited in effectiveness, giving rise to undesired aftermaths. Consequently, there is an apparent demand for more efficient and effective solutions to addressing feedback problems in hearing aids. This research was therefore centred on developing a functional signal processing algorithm using the Spectral Subtraction Technique, SST. In this research, noise samples were collected from four different sources, including a Hospital in South-Western Nigeria, so that simulations and analyses were conducted for performance evaluation of selected scenarios of noise types and audio recordings, using SST. The simulations were implemented using a Python-based approach, aided by the power of digital signal processing algorithms. Results from the simulation revealed the effectiveness of SST in background noise reduction, with improved signal–to–noise ratio (SNR) in the different scenarios, including speech recordings with background chatter, calm pop songs with street traffic noise and public speeches with air conditioning noise. In conclusion, the SST offers a practical approach to noise reduction in audio signals. While the code offers users an effective tool for reducing noise in audio recordings and enhancing audio quality, its simplicity and clarity make it accessible to users with varying expertise in audio signal processing.