Informed Source Separation for Turbofan Broadband Noise Using Non-Negative Matrix Factorization
Bibliographic record
Abstract
The rising concern in the aircraft industry regarding engine noise has led to the use of source separation techniques to target future noise reduction efforts.This paper investigates the use of Non-negative Matrix Factorization (NMF) as an automatic source separation method for engine noise, using an array of microphones.Turbofan broadband noise is a complex mixture of sounds generated by individual sources which have a specific spectrum and directivity.The objective of this study is to assess the separation performance of the method and the relevance of additional expert knowledge in the form of a regularization term.The method was applied to a set of simulated engine noises at a certification flight point, and the resulting separated sources were analyzed for their spectral and spatial characteristics.Results indicate that NMF can effectively separate the individual sources of engine noise, even when the sources have similar characteristics.In the case of low power sources, information is missing and regularization significantly improved the separation performance.NMF appears to be a promising method for source separation of turbofan broadband noise.Further validation should be obtained from a more complex corpus with better spectral resolution.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".