Acoustic source localization with eye array
Bibliographic record
Abstract
A novel signal-processing algorithm and array for sound source localization (SSL) in three-dimensional space is presented. This method, which has similarity to the eye in localization of light rays, consists of a novel hemispherical microphone array with 26 microphones on the shell and one microphone in the sphere center. The microphones on the shell map a geodesic hemisphere called two-frequency icosahedron; hence, each microphone has at least four other orthogonal microphones. A signal-processing scheme utilizes parallel creation of a special closeness function for each microphone direction on the shell in the time domain. Each closeness function cell (lens cell) consists of center microphone, shell microphone, and one of its orthogonals. The closeness function output values are linearly proportional to spatial angular difference between the sound source direction and each of the shell microphone directions. By choosing microphone directions corresponding to the highest closeness function values and implementing a linear weighted spatial averaging on them, the sound source direction is estimated. Contrary to traditional SSL techniques, this method is based on simple parallel mathematical calculations in the time domain with low computational costs. The laboratory implementation of the array and algorithm shows reasonable accuracy in a reverberant room.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".