AI-Driven Near-Lossless Audio Compression Modeling via Autoencoders

Near-Lossless audio compression is an important aspect of efficient data storage and transmission in various audio-related applications. Traditional compression algorithms often rely on mathematical techniques and signal processing methods to reduce file size while maintaining the original audio quality. However, deep learning-based methods have shown promising results in achieving better compression performance. This study explores the application of deep learning techniques for Near-Lossless audio compression. Deep neural networks (DNNs) and recurrent neural networks (RNNs) are used to learn compressed representations of audio data that can be efficiently reconstructed without any information loss. Models have been trained on a large dataset of unannotated audio samples to capture complex patterns and dependencies in the data. Experimental results demonstrated a compression ratio of 0.0333 (30:1) with a mean squared error (MSE) of 4.3957e-06, outperforming traditional compression algorithms such as FLAC (compression ratio: 0.1879) in both compression efficiency and reconstruction quality. In addition, the trained models showed robust generalization to unseen audio samples. Overall, this study contributes to the advancement of Near-Lossless audio compression techniques using deep learning methodologies.