Efficient audio declipping using regularized least squares

While many recently proposed audio declipping algorithms are highly effective in their ability to restore clipped speech, the algorithms´ computational complexities inhibit their use in many practical situations. Real-time or nearly real-time performance is impossible using a typical laptop computer, with some algorithms taking as long as 400 times the actual duration of the input to complete restoration. This paper introduces a novel declipping algorithm, referred to as Regularized Blind Amplitude Reconstruction, which is capable of restoring clipped audio at rates much faster than real time and at restoration qualities comparable to existing algorithms. The quality of declipping is evaluated in terms of automatic speech recognition performance on declipped speech, as well as the degree to which each declipping algorithm improves the audio´s signal-to-noise ratio.