Ontology and rule based retrieval of sound objects in augmented audio reality system for museum visitors
Bibliographic record
Abstract
ec(h)o is an "augmented reality interface" utilizing spatialized soundscapes and a semantic web approach to information. The initial prototype is designed for a natural history and science museum. The platform is designed to create a museum experience that consists of a physical installation and an interactive virtual layer of three-dimensional soundscapes that are physically mapped to the museum displays. The source for the audio data is digital sound objects. The digital objects originate in a network of object repositories that connect digital content from one museum with other museums collections. The interface enables people to interact with the system by movement and object manipulation-based gestures without the direct use of a computer device. The focus of this paper is the retrieval mechanism for the sound objects for the museum visitor. The retrieval mechanism is built on the user model and conceptual descriptions of the sound object and museum artifacts in the form of ontologies for sound and psychoacoustics, topic ontology and Conceptual Reference Model for museum information. The retrieval criteria are represented as inference rules that represent knowledge from psychoacoustics, cognitive domain and composition aspects of interaction. The system will be demonstrated in exhibition space in Nature Museum in Ottawa in January 2003.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".