Elimination of clicks from archive speech signals using sparse autoregressive modeling

This paper presents a new approach to elimination of impulsive disturbances from archive speech signals. The proposed sparse autoregressive (SAR) signal representation is given in a factorized form — the model is a cascade of the so-called formant filter and pitch filter. Such a technique has been widely used in code-excited linear prediction (CELP) systems, as it guarantees model stability. After detection of noise pulses using linear prediction, the factorized model is converted into a generic sparse form in order to perform a projection-based signal interpolation. It is shown that the proposed algorithm is able to deal favorably with speech signals with strong glottal activity, which is a serious problem for algorithms based on the classical AR modeling.