Measurement of Harmonic Distortion Audibility Using a Simplified Psychoacoustic Model - Updated

Temme S, Brunet P, Qarabaqi P. Measurement of Harmonic Distortion Audibility Using a Simplified Psychoacoustic Model - Updated. In: AES Conference: 51st International Conference: Loudspeakers and Headphones. Audio Engineering Society; 2013. Paper 1-5. Available from: https://aes.org/publications/elibrary-page/?id=16885

@inproceedings{Temme2013_16885,
  author = {Temme, Steve and Brunet, Pascal and Qarabaqi, Parastoo},
  title = {{Measurement of Harmonic Distortion Audibility Using a Simplified Psychoacoustic Model - Updated}},
  booktitle = {AES Conference: 51st International Conference: Loudspeakers and Headphones},
  note = {Paper 1-5},
  year = {2013},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16885}
}

TY  - CPAPER
TI  - Measurement of Harmonic Distortion Audibility Using a Simplified Psychoacoustic Model - Updated
AU  - Temme, Steve
AU  - Brunet, Pascal
AU  - Qarabaqi, Parastoo
T2  - AES Conference: 51st International Conference: Loudspeakers and Headphones
M1  - Paper 1-5
PY  - 2013
DA  - 2013/08/06
UR  - https://aes.org/publications/elibrary-page/?id=16885
PB  - Audio Engineering Society
LA  - en
AB  - A perceptual method is proposed for measuring harmonic distortion audibility. This method is similar to the CLEAR (Cepstral Loudness Enhanced Algorithm for Rub & buzz) algorithm previously proposed by the authors as a means of detecting audible Rub & Buzz which is an extreme type of distortion[1,2]. Both methods are based on the Perceptual Evaluation of Audio Quality (PEAQ) standard[3]. In the present work, in order to estimate the audibility of regular harmonic distortion, additional psychoacoustic variables are added to the CLEAR algorithm. These variables are then combined using an artificial neural network approach to derive a metric that is indicative of the overall audible harmonic distortion. Experimental results on headphones are presented to justify the accuracy of the model.
ER  -