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Predicting the perceived level of reverberation using machine learning

Safavi, Saeid, Pearce, Andy, Wang, Wenwu and Plumbley, Mark (2018) Predicting the perceived level of reverberation using machine learning In: 52nd Asilomar Conference on Signals, Systems and Computers (ACSSC 2018), 28-31 Oct 2018, Asilomar Conference Grounds, Pacific Grove, California, USA.

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Abstract

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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Safavi, Saeids.safavi@surrey.ac.uk
Pearce, Andyandy.pearce@surrey.ac.uk
Wang, WenwuW.Wang@surrey.ac.uk
Plumbley, Markm.plumbley@surrey.ac.uk
Date : 2018
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Reverberation time; Human subject test; Machine learning; MLP
Related URLs :
Depositing User : Clive Harris
Date Deposited : 03 Oct 2018 14:43
Last Modified : 28 Oct 2018 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/849528

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