Real-Time Lossy Audio Signal Reconstruction Using Novel Sliding Based Multi-instance Linear Regression/Random Forest and Enhanced CGPANN

Khan, Nadia Masood; Khan, Gul Muhammad

doi:10.1007/s11063-020-10379-5

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…In the work of Khan et al [4], a modern neuro-evolution algorithm, Enhanced Cartesian Genetic Programming Evolved Artificial Neural Network (ECGPANN), was proposed by the authors as a predictor of the lost signal samples in real-time. The authors have trained and tested the algorithms on audio speech signal data and evaluated them on the music signal.…”

Section: Related Workmentioning

confidence: 99%

ARMAS: Active Reconstruction of Missing Audio Segments

Cheddad¹,

Cheddad²

2021

Preprint

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Digital audio signal reconstruction of lost or corrupt segment using deep learning algorithms has been explored intensively in the 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 the reconstruction of 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-theart shallow (RF-Random Forest and SVR-Support Vector Regression) and deep learning (LSTM-Long Short-Term Memory) methods. The results (including comparison to the SPAIN and Autoregressive methods) are evaluated with four 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 (noisy-latent representation) steganography provides. This work may trigger interest in the optimization of this approach and/or in transferring it to different domains (i.e., image reconstruction).

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Section: Related Workmentioning

confidence: 99%