A Comprehensive Approach for Denoising and Securing Audio Data with U-Net and Kyber

This paper introduces a comprehensive methodology for denoising audio datasets through the utilization of the U-shaped neural network (U-Net) architecture, leading to notable enhancements in audio signal quality across diverse datasets. We observe considerable reductions in root mean square error (RMSE), sum of squared errors (SSE), and mean absolute error (MAE), along with significant improvements in peak signal-to-noise ratio (PSNR), highlighting the effectiveness of our denoising process. The structural similarity index (SSIM) values further confirm the preservation of structural integrity in the denoised audio signals. Moreover, the integration of Kyber encryption and decryption has demonstrated efficiency in processing times, ensuring data privacy without imposing significant computational overhead. This integrated approach presents a compelling solution for both elevating audio data quality and upholding data security, rendering it suitable for real-time applications.