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Subjective audio quality evaluation of embedded-optimization-based distortion precompensation algorithms.

Bruno Defraene1, Toon van Waterschoot1, Moritz Diehl1

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Embedded-optimization precompensation algorithms significantly enhance audio quality by reducing linear and nonlinear distortion. Objective PEAQ scores correlate well with subjective evaluations, validating its use for predicting audio quality impacts.

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Area of Science:

  • Acoustics and Signal Processing
  • Audio Engineering
  • Perceptual Audio Quality

Background:

  • Audio signals are susceptible to linear and nonlinear distortions, impacting perceived quality.
  • Existing methods for distortion mitigation may not fully address complex distortion types.
  • Objective metrics are needed to reliably predict subjective audio quality.

Purpose of the Study:

  • To evaluate embedded-optimization-based precompensation algorithms for audio distortion mitigation.
  • To assess the impact of these algorithms on both linear and nonlinear distortion.
  • To validate the use of the Perceptual Evaluation of Audio Quality (PEAQ) metric.

Main Methods:

  • Conducted subjective audio quality evaluation experiments.
  • Applied embedded-optimization-based precompensation algorithms to audio signals with distortions.
  • Collected subjective quality scores and objective PEAQ scores.

Main Results:

  • Statistically significant improvement in perceived audio quality was observed.
  • The algorithms effectively mitigated nonlinear distortion and combined linear/nonlinear distortion.
  • A significant positive correlation was found between subjective and objective PEAQ scores.

Conclusions:

  • Embedded-optimization precompensation is effective for improving audio quality in the presence of distortion.
  • PEAQ is a valid tool for predicting the perceptual impact of audio distortion.
  • The findings support the use of these algorithms in audio processing applications.