Perceptual Similarities between Artificial Reverberation Algorithms and Real Reverberation

Abstract

This paper presents a study evaluating the perceptual similarity between artificial reverberation algorithms and acoustic measurements. An online headphone-based listening test was conducted and data were collected from 20 expert assessors. Seven reverberation algorithms were tested in the listening test, including the Dattorro, Directional Feedback Delay Network (DFDN), Feedback Delay Network (FDN), Gardner, Moorer, and Schroeder reverberation algorithms. A new Hybrid Moorer–Schroeder (HMS) reverberation algorithm was included as well. A solo cello piece, male speech, female singing, and a drumbeat were rendered with the seven reverberation algorithms in three different reverberation times (0.266 s, 0.95 s and 2.34 s) as the test conditions. The test was conducted online and based on the Multiple Stimuli with Hidden Reference and Anchor (MUSHRA) paradigm. The reference conditions consisted of the same audio samples convolved with measured binaural room impulse responses (BRIRs) with the same three reverberation times. The anchor was dual-mono 3.5 kHz low pass filtered audio. The similarity between the test audio and the reference audio was scored on a scale of zero to a hundred. Statistical analysis of the results shows that the Gardner and HMS reverberation algorithms are good candidates for exploration of artificial reverberation in Augmented Reality (AR) scenarios in future research.