Objective Assessment of Perceptual Audio Quality Using ViSQOLAudio
By Colm Sloan, Naomi Harte, Damien Kelly, Anil C. Kokaram, Andrew Hines. 2017
44 citations recorded by the source corpus.
Digital audio broadcasting services transmit substantial amounts of data that is encoded to minimize bandwidth whilst maximizing user quality of experience. Many large service providers continually alter codecs to improve the encoding process. Performing subjective tests to validate each codec alteration would be impractical, necessitating the use of objective perceptual audio quality models. This paper evaluates the quality scores from ViSQOLAudio, an objective perceptual audio quality model, against the quality scores of PEAQ, POLQA, and PEMO-Q on three datasets containing fullband audio encoded with a variety of codecs and bitrates. The results show that ViSQOLAudio was more accurate than all other models on two of the datasets and performed well on the third, demonstrating the utility of ViSQOLAudio for predicting the perceptual audio quality for encoded music.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
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Citations in the corpus
- Audio Codec Enhancement with Generative Adversarial Networks (2020, neutral): “measures predicted by ViSQOLAudio (ViA) [30].”
- Jitter Buffer Compensation in Voice over IP Quality Estimation (2019, background): “adding support for Wideband speech [5], [6], audio quality [7] and spatial audio [8]) but did not address the problem of signal mis-alignment caused by jitter buffers.”