Sweet [re]production: developing sound spatialization tools for musical applications with emphasis on sweet spot and off-center perception
Bibliographic record
Abstract
This dissertation investigates spatial sound production and reproduction technology as a mediator between music creator and listener. Listening experiments investigate the perception of spatialized music as a function of the listening position in surround-sound loud- speaker setups.Over the last 50 years, many spatial sound rendering applications have been developed and proposed to artists. Unfortunately, the literature suggests that artists hardly exploit the possibilities offered by novel spatial sound technologies. Another typical drawback of many sound rendering techniques in the context of larger audiences is that most listeners perceive a degraded sound image: spatial sound reproduction is best at a particular listening position, also known as the sweet spot.Structured in three parts, this dissertation systematically investigates both problems with the objective of making spatial audio technology more applicable for artistic purposes and proposing technical solutions for spatial sound reproductions for larger audiences.The first part investigates the relationship between composers and spatial audio technology through a survey on the compositional use of spatialization, seeking to understand how composers use spatialization, what spatial aspects are essential and what functionalities spatial audio systems should strive to include.The second part describes the development process of spatializaton tools for musical applications and presents a technical concept. The Virtual Microphone Control (ViMiC) system is an auditory virtual environment that recreates a recording situation through virtual sound sources, virtual room properties and virtual microphones. A technical concept is presented to facilitate artistic work with spatial audio systems and to allow the combination of different spatialization tools.The third part investigates the perception of spatialized sounds as a function of the listening positions in multichannel sound systems. Perceptual experiments were designed to understand the multidimensional nature of an off-center sound degradation and to propose concepts to improve the listening conditions for larger audiences.This research extends our understanding of spatial audio perception and has potential value to all those interested in spatial audio quality, including designers, creators and specialists in the fields of acoustics, music, technology and auditory perception.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".