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.

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Decorrelation of audio signals is a critical step for spatial sound reproduction on multichannel configurations. Correlated signals yield a focused phantom source between the reproduction loudspeakers and may produce undesirable comb-filtering artifacts when the signal reaches the listener with small phase differences. Decorrelation techniques reduce such artifacts and extend the spatial auditory image by randomizing the phase of a signal while minimizing the spectral coloration. This paper proposes a method to optimize the decorrelation properties of a sparse noise sequence, called velvet noise, to generate short sparse FIR decorrelation filters. The sparsity allows a highly efficient time-domain convolution. The listening test results demonstrate that the proposed optimization method can yield effective and colorless decorrelation filters. In comparison to a white noise sequence, the filters obtained using the proposed method preserve better the spectrum of a signal and produce good quality broadband decorrelation while using 76% fewer operations for the convolution. Satisfactory results can be achieved with an even lower impulse density which decreases the computational cost by 88%.

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Artificial reverberation is an audio effect used to simulate the acoustics of a space while controlling its aesthetics, particularly on sounds recorded in a dry studio environment. Delay-based methods are a family of artificial reverberators using recirculating delay lines to create this effect. The feedback delay network is a popular delay-based reverberator providing a comprehensive framework for parametric reverberation by formalizing the recirculation of a set of interconnected delay lines. However, one known limitation of this algorithm is the initial slow build-up of echoes, which can sound unrealistic, and overcoming this problem often requires adding more delay lines to the network. In this paper, we study the effect of adding velvet-noise filters, which have random sparse coefficients, at the input and output branches of the reverberator. The goal is to increase the echo density while minimizing the spectral coloration. We compare different variations of velvet-noise filtering and show their benefits. We demonstrate that with velvet noise, the echo density of a conventional feedback delay network can be exceeded using half the number of delay lines and saving over 50% of computing operations in a practical configuration using low-order attenuation filters.

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