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Active acoustic enhancement systems (AAESs) alter the perceived acoustics of a space by using microphones and loudspeakers to introduce sound energy into the room. Double-sloped energy decay may be observed in these systems. However, it is unclear as to which conditions lead to this effect, and to what extent double sloping reduces the perceived naturalness of the reverberation compared to Sabine decay. This paper uses simulated combinations of AAES parameters to identify which cases affect the objective curvature of the energy decay. A subjective test with trained listeners assessed the naturalness of these conditions. Using an AAES model, room impulse responses were generated for varying room dimensions, absorption coefficients, channel counts, system loop gains and reverberation times (RTs) of the artificial reverberator. The objective double sloping was strongly correlated to the ratio between the reverberator and passive room RTs, but parameters such as absorption and room size did not have a profound effect on curvature. It was found that double sloping significantly reduced the perceived naturalness of the reverberation, especially when the reverberator RT was greater than two times that of the passive room. Double sloping had more effect on the naturalness ratings when subjects listened to a more absorptive passive room, and also when using speech rather than transient stimuli. Lowering the loop gain by 9 dB increased the naturalness of the doublesloped stimuli, where some were rated as significantly more natural than the Sabine decay stimuli from the passive room.

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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.

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