On the use of modified phase transform weighting functions for acoustic imaging with the generalized cross correlation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The generalized cross correlation (GCC) is an efficient technique for performing acoustic imaging. However, it suffers from important limitations such as a large main lobe width for noise sources with low frequency content or a high amplitude of side lobes for noise sources with high frequencies. Prefiltering operation of the microphone signals by a weighting function can be used to improve the acoustic image. In this work, two weighting functions based on PHAse Transform (PHAT) improvements are used. The first adds an exponent to the PHAT expression (ρ-PHAT), while the second adds the minimum value of the coherence function to the denominator (ρ-PHAT-C). Numerical acoustic images obtained with the GCC and those weighting functions are compared and quantitatively assessed thanks to a metric based on a covariance ellipse, which surrounds either the main lobe or the side lobes. The weighting function ρ-PHAT-C provides the smallest surface ellipses especially when the arithmetic of the GCC is replaced by the geometric mean (GEO). Experimental measurements are carried out in a hemi-anechoic room and a reverberant chamber where two loudspeakers were set in front of microphone array. The acoustic images obtained confirm that the ρ-PHAT-C with the GEO outperforms the GCC, GCC-PHAT, and GCC ρ-PHAT.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it