Cascaded dynamic noise reduction utilizing VAD to improve residual suppression

Speech processing systems often operate in noisy and reverberant environments. Their operation is subject to the accuracy of the underlying noise reduction algorithm, that aims to reduce noise present in the signals that are captured by the employed microphones. Under adverse conditions a noise reduction scheme, failing to perform adequately, will produce results characterised by speech distortions (metallic or clipping voice) and/or fluctuating residual background noises, the result of inaccuracy in estimating the noise spectrum, known as musical noise. In this paper, performance enhancement when employing a statistical model based Voice Activity Detector (VAD) in combination with noise reduction, is presented in terms of residual noise suppression within silence intervals and short pauses during speech production. To this end, an efficient noise reduction architecture has been developed relying on cascading a previously presented presented denoising scheme. This way, we initially subtract primary noise and then consequently use the same technique, with specific parameter adjustments, to remove spectral subtraction artefacts such as musical noise or noise leftovers that were generated by the first stage. Simulations under various noise types and intensities indicate significant performance enhancement when employing the proposed system.