Comparative study for Active Noise Cancellation using Adaptive filtering and Standing wave pattern
Bibliographic record
Abstract
Noise pollution is one of the most fundamental challenges facing our environment, causes health problem, communication inefficiency and degrade the performance of works due to lack of concentration, thus, mitigating this impact becomes an unavoidable requirement of time to protect people's health and the environment. This noise may originate from several sources including industrial machinery, system parts wear out, and adjacent environmental acoustics. To mitigate this noise effect, an Active Noise Cancellation (ANC) headphone is achieved by two effective techniques; Adaptive filtering and Standing wave phenomenon. In this work, an ANC system is designed using both adaptive filtering and standing wave techniques, the former one basically utilizes single-channel feedforward whereas the latter one utilizes both single-channel feedforward and feedback control. LMS adaptive filter algorithm is the basic component of the designed ANC headphone. For simulation, a noise-free signal will be used as the desired audio signal and a gaussian distributed noise as the unwanted noise signal, these are combined to form noise corrupted speech signal. Propose algorithms performance were evaluated based on the ability to mitigate effects of different frequency broad-band noise signals and of different Noise to Signal ratio. Evaluation measures used are; convergence rate and noise reduction in dB. Result reveals ANC headphone using standing wave technique has better performance at mitigating noise frequency below 800Hz, with low SNR than Adaptive filtering. However, at higher frequencies above 1000Hz, ANC headphone using Adaptive filtering has good performance of masking high frequencies up to 22dB.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".