Predicting the Perceived Level of Reverberation using Features from Nonlinear Auditory Model

Abstract

Perceptual measurements have typically been recog- nized as the most reliable measurements in assessing perceived levels of reverberation. In this paper, a combination of blind RT60 estimation method and a binaural, nonlinear auditory model is employed to derive signal-based measures (features) that are then utilized in predicting the perceived level of rever- beration. Such measures lack the excess of effort necessary for calculating perceptual measures; not to mention the variations in either stimuli or assessors that may cause such measures to be statistically insignificant. As a result, the automatic extraction of objective measurements that can be applied to predict the perceived level of reverberation become of vital significance. Consequently, this work is aimed at discovering measurements such as clarity, reverberance, and RT60 which can automatically be derived directly from audio data. These measurements along with labels from human listening tests are then forwarded to a machine learning system seeking to build a model to estimate the perceived level of reverberation, which is labeled by an expert, autonomously. The data has been labeled by an expert human listener for a unilateral set of files from arbitrary audio source types. By examining the results, it can be observed that the automatically extracted features can aid in estimating the perceptual rates.

Keywords

• Audio signal processing
• Reverberation
• Feature extraction
• Human experiments
• machine learning