Download Differentiable Scattering Delay Networks for Artificial Reverberation Scattering delay networks (SDNs) provide a flexible and efficient
framework for artificial reverberation and room acoustic modeling. In this work, we introduce a differentiable SDN, enabling
gradient-based optimization of its parameters to better approximate the acoustics of real-world environments. By formulating
key parameters such as scattering matrices and absorption filters
as differentiable functions, we employ gradient descent to optimize an SDN based on a target room impulse response. Our approach minimizes discrepancies in perceptually relevant acoustic
features, such as energy decay and frequency-dependent reverberation times. Experimental results demonstrate that the learned SDN
configurations significantly improve the accuracy of synthetic reverberation, highlighting the potential of data-driven room acoustic modeling.
Download DataRES and PyRES: A Room Dataset and a Python Library for Reverberation Enhancement System Development, Evaluation, and Simulation Reverberation is crucial in the acoustical design of physical
spaces, especially halls for live music performances. Reverberation Enhancement Systems (RESs) are active acoustic systems that
can control the reverberation properties of physical spaces, allowing them to adapt to specific acoustical needs. The performance of
RESs strongly depends on the properties of the physical room and
the architecture of the Digital Signal Processor (DSP). However,
room-impulse-response (RIR) measurements and the DSP code
from previous studies on RESs have never been made open access, leading to non-reproducible results. In this study, we present
DataRES and PyRES—a RIR dataset and a Python library to increase the reproducibility of studies on RESs. The dataset contains RIRs measured in RES research and development rooms and
professional music venues. The library offers classes and functionality for the development, evaluation, and simulation of RESs.
The implemented DSP architectures are made differentiable, allowing their components to be trained in a machine-learning-like
pipeline. The replication of previous studies by the authors shows
that PyRES can become a useful tool in future research on RESs.