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.