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

arXiv: https://arxiv.org/abs/2111.10891 · HTML: https://mlsanity.com/paper/2111.10891/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 21 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Inpainting of long audio segments with similarity graphs](https://mlsanity.com/paper/1607.06667/) (2016)
- [Audio inpainting with generative adversarial network](https://mlsanity.com/paper/2003.07704/) (2020)
- [Sparsity and cosparsity for audio declipping: a flexible non-convex approach](https://mlsanity.com/paper/1506.01830/) (2015)
- [Audio Inpainting: Revisited and Reweighted](https://mlsanity.com/paper/2001.02480/) (2020)
- [Introducing SPAIN (SParse Audio INpainter)](https://mlsanity.com/paper/1810.13137/) (2018)
- [Element analysis: a wavelet-based method for analyzing time-localized events in noisy time series](https://mlsanity.com/paper/1703.04882/) (2017)

## Abstract (arXiv)

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).