Restoration of Click Degraded Speech and Music Based on High Order Sparse Linear Prediction

Clicks are localized degradation that affect most archived audio media. Click degradation are objectionable to the listener and should be suppressed to make the audio acceptable. The use of linear prediction (LP) modeling for the restoration of audio signal that has been corrupted by click degradation has been extensively researched. However, it is hampered by the need of a pitch predictor and by its poor performance for voiced speech and music. High-order sparse linear prediction has been shown to offer better representation of voiced speech and music over conventional linear prediction. In this paper, the use of l1-norm and l0-norm regularized high-order sparse linear prediction is proposed for restoration of audio signal that is corrupted by click degradation that can work equally well for speech and music without a priori information of the type of signal. High-order sparse linear prediction is used to obtain a better model of the spectral envelope and harmonics in the presence of click degradation and background noise. Evaluation with clean speech and music shows that the proposed method achieves SNR improvement from 3dB to 5dB over conventional LP approach for a wide range of click durations. Tests with speech and music corrupted by background noise in addition to click degradation show that the proposed method achieves a better SNR than the restoration of click degraded speech and music that is not corrupted by background noise using conventional LP. Perceptual evaluation of audio quality (PEAQ), used to estimate the subjective quality audio, shows that the proposed method performs better than conventional LP methods in terms of perceived quality of the restored audio by a listener. A computational requirement analysis shows that even though the proposed method is not real-time, it only takes 2 to 3 times the duration of the frame being restored on a present day general-purpose processor.