Predicting the perceived level of reverberation using machine learning

• Published in: Conference record - Asilomar Conference on Signals, Systems, & Computers pp. 27 - 30
• Main Authors: Safavi, Saeid, Pearce, Andy, Wang, Wenwu, Plumbley, Mark
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: Decision trees; Feature extraction; Human subject test; Logistics; Machine learning; MLP; Predictive models; Reverberation; Reverberation time; Training
• ISSN: 2576-2303
• DOI: 10.1109/ACSSC.2018.8645201

Perceptual measures are usually considered more reliable than instrumental measures for evaluating the perceived level of reverberation. However, such measures are time consuming and expensive, and, due to variations in stimuli or assessors, the resulting data is not always statistically significant. Therefore, an (objective) measure of the perceived level of reverberation becomes desirable. In this paper, we develop a new method to predict the level of reverberation from audio signals by relating the perceptual listening test results with those obtained from a machine learned model. More specifically, we compare the use of a multiple stimuli test for within and between class architectures to evaluate the perceived level of reverberation. An expert set of 16 human listeners rated the perceived level of reverberation for a same set of files from different audio source types. We then train a machine learning model using the training data gathered for the same set of files and a variety of reverberation related features extracted from the data such as reverberation time, and direct to reverberation ratio. The results suggest that the machine learned model offers an accurate prediction of the perceptual scores.