Comparison of windowing in speech and audio coding

Speech and audio coding have during the last decade converged to an increasingly unified technology. This contribution discusses one of the remaining fundamental differences between speech and audio paradigms, namely, windowing of the input signal. Audio codecs generally use lapped transforms and apply a perceptual model in the transform domain, whereby temporal continuity is achieved by windowing and overlap-add. Speech codecs on the other hand achieve temporal continuity by using linear predictive filtering, whereby windowing is applied in the residual domain. Despite these fundamental differences, we demonstrate that the two windowing approaches, combined with perceptual modeling, perform very similarly both in terms of perceptual quality and theoretical properties.