Leveraging CNN and principal component analysis for dynamic variance control in audio compression

Abstract

This study addresses challenges arising from large audio file storage needs and rising network bandwidth demands. In this paper, a novel audio codec design is proposed, integrating audio sample segregation, user input variance controlled principal component analysis (PCA), and Convolutional Neural Network (CNN). PCA computes sample variance feature vectors, extracts principal components, and determines compression rates. This method leverages PCA and CNN to compress audio efficiently, yielding high-quality reconstructed audio. Experimental results show that increasing PCA components generally improves PSNR values, while decreasing components may reduce CR, MSE, and other error metrics. The simulation results are analyzed and compared to other existing lossless audio encoding schemes with various statistical and robustness features.