Optimized lossless audio compression using DCT energy thresholding and machine learning technique

Abstract

This paper proposes a novel lossless audio compression technique, utilizing the Discrete Cosine Transform (DCT) coefficient-controlled technique based on energy thresholding, an XOR-based neural network compression model, and a CNN model. Initially, the DCT is applied to the input audio signal to achieve better energy compaction, followed by transforming selected DCT coefficients into a compressed binary stream. Subsequently, this binary stream is passed to two prediction-based optimized models: an XOR model and a CNN model for further compression.The binary stream is divided into two equal pieces, the data and the key. The XOR neural network model processes the data and key to produce an compressed XORed binary stream. Using a proposed CNN architecture, this stream is further compressed with latent space representations to produce compressed audio data. The simulation findings are analyzed using various statistical and robustness measures and compared with existing approaches.