An Investigation into the Relationship Between Perceived Quality-of-Experience and Virtual Acoustic Environments: the Case of 3D Audio Telephony
Bibliographic record
Abstract
Abstract: Quality of Experience (QoE) is a human centric quality evaluation method which provides the blue print of human needs, perceptions, feelings and experiences with respect to a multimedia service. In a communications ecosystem, human interac-tion takes place alongside technological, contextual, and business domains, thus pro-ducing a holistic view on QoE formation. In this paper, we investigate the relationship between human perceived QoE and “context ” for burgeoning 3-dimensional (3D) au-dio teleconferencing services. 3D audio teleconferencing applications are customizable by generating different virtual acoustic environments (VAE), where parameters such as virtual room size and competing talker conditions can be adjusted for a particular application. The impact of different VAE characteristics on perceived QoE, however, is still unknown. In this study, four QoE factors were investigated across different VAE scenarios. It was found that a) medium-size virtual rooms produce optimal perceived QoE, b) competing talkers of mixed gender could be easily located in the virtual space, and c) competing speaker gender had no significant effect on perceived audio quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".