Sebastian J. Schlecht is Professor of Practice for Sound in Virtual Reality at the Aalto University, Finland. This position is shared between the Aalto Media Lab and the Aalto Acoustics Lab. His research interests include spatial audio processing with an emphasis on artificial reverberation, synthesis, reproduction, and 6-degrees-of-freedom virtual and mixed reality applications. In particular, his research efforts have been directed towards the intersection of mathematical filter design, efficient algorithms, perceptual aspects, and sound design. Current open and ongoing student projects can be found here.
PhD in Acoustic Signal Processing, 2017
University of Erlangen-Nuremberg, Germany
M.Sc. in Digital Music Processing, 2011
Queen Mary, University of London, UK
M.Sc. in Applied Mathematics, 2010
University of Trier, Germany
Feedback delay networks (FDNs) are recursive filters, which are widely used for artificial reverberation and decorrelation. While there exists a vast literature on a wide variety of reverb topologies, this work aims to provide a unifying framework to design and analyze delay-based reverberators. To this end, we present the Feedback Delay Network Toolbox (FDNTB), a collection of the MATLAB functions and example scripts. The FDNTB includes various representations of FDNs and corresponding translation functions. Further, it provides a selection of special feedback matrices, topologies, and attenuation filters. In particular, more advanced algorithms such as modal decomposition, time-varying matrices, and filter feedback matrices are readily accessible. Furthermore, our toolbox contains several additional FDN designs. Providing MATLAB code under a GNU-GPL 3.0 license and including illustrative examples, we aim to foster research and education in the field of audio processing.
Feedback delay networks (FDNs) are recursive filters, which are widely used for artificial reverberation and decorrelation. One central challenge in the design of FDNs is the generation of sufficient echo density in the impulse response without compromising the computational efficiency. In a previous contribution, we have demonstrated that the echo density of an FDN can be increased by introducing so-called delay feedback matrices where each matrix entry is a scalar gain and a delay. In this contribution, we generalize the feedback matrix to arbitrary lossless filter feedback matrices (FFMs). As a special case, we propose the velvet feedback matrix, which can create dense impulse responses at a minimal computational cost. Further, FFMs can be used to emulate the scattering effects of non-specular reflections. We demonstrate the effectiveness of FFMs in terms of echo density and modal distribution.