Spatial Sound Reproduction Based on HRTF Auto-Selection Algorithm
Bibliographic record
Abstract
The desired beam pattern of the array can be achieved by applying specific array weighting in the eigenbeam space derived from the plane wave decomposition. Head related transfer function (HRTF) data are selected automatically from the KEMAR HRTF measurements database of MIT media lab without the message of the direction of the incoming sound wave. The HRTF for the sound wave direction are derived by combining the directivity of the eigenbeam beamforming and the algebraic expression of HRTF. Computer simulations demonstrate that the HRTF approximation derived from this paper are similar to those of the measurements by MIT media lab in certain frequency band. The higher the order of the eigenbeam used in eigenbeam beamforming, the more similar of the derived HRTF data with those of the measurements. Listening experiments are conducted to evaluate the efficiency of the proposed HRTF auto-selection algorithm subjectively. The results indicate that the audio localization precision for virtual sounds using approximated HRTF is consistent with those of the measured HRTF, which verifies the validity of the proposed algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".