Velvet Noise Decorrelator

Benoit Alary; Archontis Politis; Vesa Välimäki
DAFx-2017 - Edinburgh
Decorrelation of audio signals is an important process in the spatial reproduction of sounds. For instance, a mono signal that is spread on multiple loudspeakers should be decorrelated for each channel to avoid undesirable comb-filtering artifacts. The process of decorrelating the signal itself is a compromise aiming to reduce the correlation as much as possible while minimizing both the sound coloration and the computing cost. A popular decorrelation method, convolving a sound signal with a short sequence of exponentially decaying white noise which, however, requires the use of the FFT for fast convolution and may cause some latency. Here we propose a decorrelator based on a sparse random sequence called velvet noise, which achieves comparable results without latency and at a smaller computing cost. A segmented temporal decay envelope can also be implemented for further optimizations. Using the proposed method, we found that a decorrelation filter, of similar perceptual attributes to white noise, could be implemented using 87% less operations. Informal listening tests suggest that the resulting decorrelation filter performs comparably to an equivalent white-noise filter.