PEMO-Q—A New Method for Objective Audio Quality Assessment Using a Model of Auditory Perception
By R. Huber, B. Kollmeier. 2006
357 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Cognitive Neuroscience, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- Hakan Erdogan
- Naomi Harte
- Emmanuel Vincent
- Volker Hohmann
- Valentin Emiya
- Sharon Gannot
- Shmulik Markovich Golan
- Scott Wisdom
In Cognitive Neuroscience
- John R. Hershey
- Ross Cutler
- Chandan K. A. Reddy
- Jonathan Le Roux
- Scott Wisdom
- Hakan Erdogan
- Anil C. Kokaram
- Andrew Hines
In Signal Processing
- John R. Hershey
- Jonathan Le Roux
- Hakan Erdogan
- Scott Wisdom
- Ross Cutler
- Chandan K. A. Reddy
- Shinji Watanabe
- Andrew Hines
Citations in the corpus
- ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric (2020, neutral): “, PEAQ [5] and PEMO-Q [6].”
- SDR – Half-baked or Well Done? (2018, methodology): “While the merits of various objective measures such as PESQ [12], Loizou’s composite measure [13], PEMO-Q [14], PEASS [15], or STOI [16], could be debated and compared, we are concerned here with an issue with the way the widely relied upon BSS eval toolbox [17] has been used.”
- Subjective and Objective Quality Assessment of Audio Source Separation (2011, methodology): “Two series of scatter plots are shown depending on the salience criteria defined hereafter in Section IV-C: either the energy ratios given by (11)–(13) or the features and obtained from the auditory-based PEMO-Q metric [ 35 ] in (15)–(17).”
- Intrusive and Non-Intrusive Perceptual Speech Quality Assessment Using a Convolutional Neural Network (2019, methodology): “A comparison with alternative metrics, including PEMO-Q [21] and HASQI [22], as well as an analysis of the proposed model’s ability to generalize to unseen types of speech distortion is left for future work.”
- A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, positive): “Implemented metrics PESQ [299] perceptual speech quality (PESQ) PEMO-Q [300] perceptual similarity metric (PSM) STOI [301] 27 short-time objective intelligibility (STOI) Loizou’s [11] implemented in Roomsimove 19 and Signal generator 20 .”
- Speech intelligibility prediction using a Neurogram Similarity Index Measure (2012, methodology): “Huber and Kollmeier (2006) used the Dau et al. (1996) auditory model to develop PEMO-Q, an audio quality assessment.”
- Objective Assessment of Perceptual Audio Quality Using ViSQOLAudio (2017, methodology): “Another model for predicting perceptual audio quality, PEMO-Q [3], was shown by its authors to predict quality more accurately than PEAQ [3].”