Ambisonics and blind source separation in virtual acoustics: Sound field reproduction of separated sources
Bibliographic record
Abstract
Blind source separation (BSS) has many applications: sound scene analysis, speech recognition, medical signal processing, etc. However, most of these applications concern the temporal separation of signals. Studies have shown the effectiveness of separation in the ambisonic domain with spherical microphone recordings. Thanks to the ambisonic approach, it is possible to separate the directions of arrival of the sources. As such, BSS becomes a promising tool for sound field reproduction with loudspeakers arrays (WaveField Sythesis or Higher-Order Ambisonics). Thanks to spherical microphone arrays and Ambisonics principle, both spatial and temporal information are available. Therefore, it would be possible to reproduce the individual sound field of each separated source. Thus, one can remove a given source from a recording and reproduce the remaining sound field. The main objective of this work is to reproduce the sound field of one of the captured sources by removing the rest of it (sources or noise). The first part of the paper presents the methods for the BSS and corresponding sound field reproduction. The second part presents simulation results and investigates effect of measurement noise, spatial source separation, and reflection. [Work supported by NSERC Discovery grant.]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".