PEAQ - The ITU Standard for Objective Measurement of Perceived Audio Quality

By T. Thiede, W. Treurniet, R. Bitto, Christian Schmidmer, T. Sporer, J. Beerends, Catherine Colomes. 2000

476 citations recorded by the source corpus.

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Citations in the corpus

ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric (2020, neutral): “, PEAQ [5] and PEMO-Q [6].”
Objective Assessment of Perceptual Audio Quality Using ViSQOLAudio (2017, methodology): “tive models were selected as each model has been shown to accurately predict perceptual quality for musical audio [3]–[6].”
Expressive TTS Training With Frame and Style Reconstruction Loss (2020, methodology): “Several computational models have been proposed to approximate human perception of audio quality, such as Perceptual Evaluation of Audio Quality (PEAQ) [41], Perceptual Evaluation of Speech Quality (PESQ) [42], and Perceptual Evaluation of Audio methods for Source Separation (PEASS) [43].”
Perceptual coding of digital audio (2000, methodology): “by several research teams, the ITU-R recently adopted an automatic perceptual measurement system, ITU-R BS-1387 [397], [402], [403] intended to assist in the tasks of codec selection, evaluation, and maintenance.”
Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs (2001, methodology): “Another model by Beerends and Stemerdink, the perceptual audio quality measure (PAQM) [4], was combined with several different audio models to produce a method known as perceptual evaluation of audio quality (PEAQ), which became ITU-R recommendation BS.1387 in 1999 [5] [6].”
Perceptual Objective Listening Quality Assessment (POLQA), The Third Generation ITU-T Standard for End-to-End Speech Quality Measurement Part I-Temporal Alignment (2013, methodology): “For the assessment of music signals at 48 kHz sampling frequency, the PEAQ (Perceptual Evaluation of Audio Quality) [12] algorithm according to ITU-R BS.”
AMBIQUAL - a full reference objective quality metric for ambisonic spatial audio (2018, background): “Objective metrics exist for speech (POLQA [12]) and audio quality (PEAQ [13]) but no objective metrics are agreed upon for spatial audio quality evaluation (although work was began on extending PEAQ [14]).”
Singing Voice Separation with Deep U-Net Convolutional Networks (2017, unknown): “Several alternatives exist to 7-step Likert scale, e.g. the ITU-R scale [28].”
PEMO-Q—A New Method for Objective Audio Quality Assessment Using a Model of Auditory Perception (2006, methodology): “It “combines concepts and output variables of most previously known measurement methods of this nature” [9], namely: DIX [7], NMR [34], OASE [8], PAQM [4], PERCEVAL [5], [35], [36], POM [6], and Toolbox (unpublished).”
Improving GANs for Speech Enhancement (2020, result): “This result tends to agree with the finding in psychoacoustics [35].”