Active Restoration of Lost Audio Signals Using Machine Learning and Latent Information

TL;DR

This paper introduces a novel audio signal reconstruction framework combining steganography, dithering, and machine learning, demonstrating improved performance over traditional and existing deep learning methods in reconstructing heavily compressed audio data.

Contribution

It proposes the HCR framework that fuses steganography, halftoning, and machine learning for enhanced audio reconstruction, a novel approach not previously explored.

Findings

HCR outperforms existing methods in SNR, ODG, and Hansen's metric.

The approach effectively reconstructs heavily compressed audio signals.

Proposed method surpasses traditional algorithms and recent deep learning models.

Abstract

Digital audio signal reconstruction of a lost or corrupt segment using deep learning algorithms has been explored intensively in recent years. Nevertheless, prior traditional methods with linear interpolation, phase coding and tone insertion techniques are still in vogue. However, we found no research work on reconstructing audio signals with the fusion of dithering, steganography, and machine learning regressors. Therefore, this paper proposes the combination of steganography, halftoning (dithering), and state-of-the-art shallow and deep learning methods. The results (including comparing the SPAIN, Autoregressive, deep learning-based, graph-based, and other methods) are evaluated with three different metrics. The observations from the results show that the proposed solution is effective and can enhance the reconstruction of audio signals performed by the side information (e.g., Latent representation) steganography provides. Moreover, this paper proposes a novel framework for reconstruction from heavily compressed embedded audio data using halftoning (i.e., dithering) and machine learning, which we termed the HCR (halftone-based compression and reconstruction). This work may trigger interest in optimising this approach and/or transferring it to different domains (i.e., image reconstruction). Compared to existing methods, we show improvement in the inpainting performance in terms of signal-to-noise ratio (SNR), the objective difference grade (ODG) and Hansen's audio quality metric. In particular, our proposed framework outperformed the learning-based methods (D2WGAN and SG) and the traditional statistical algorithms (e.g., SPAIN, TDC, WCP).