A novel fuzzy logic-based metric for audio quality assessment: Objective audio quality assessment

TL;DR

This paper introduces FQI, a fuzzy logic-based metric integrated into the PEAQ model, which slightly improves objective audio quality assessment by reducing computational complexity and enhancing performance.

Contribution

The paper proposes a new fuzzy logic-based metric, FQI, that enhances the PEAQ model's accuracy and efficiency in objective audio quality assessment.

Findings

FQI slightly outperforms the original PEAQ model.

FQI reduces computational complexity of audio quality assessment.

Integrated FQI improves the accuracy of perceived audio quality estimation.

Abstract

ITU-R BS.1387 states a method for objective assessment of perceived audio quality. This Recommendation, known also as PEAQ (Perceptual Evaluation of Audio Quality) is based on a psychoacoustic model of the human ear and was standardized by the International Telecommunications Union as an alternative to subjective tests, which are expensive and time-consuming processes. PEAQ combines various physiological and psycho-acoustical properties of the human ear to give a measure of the quality difference between a reference audio and a test audio. The reference audio signal could be considered as a distortion-free source, whereas the test signal is a distorted version of the reference, which may have audible artifacts because of compression. The algorithm computes the Model Output Variables (MOVs) which are mapped to a single quality measure, Objective Difference Grade (ODG), using a three-layer perceptron artificial neural network. The ODG estimates the perceived distortion between both audio signals. In this paper we propose a new metric of low computational complexity called FQI (Fuzzy Quality Index) which is based on Fuzzy Logic reasoning and has been incorporated into the existing PEAQ model to improve its overall performance. Results show that the modified version slightly outperforms PEAQ.