A Z-Test-Based Evaluation of a Least Mean Square Filter for Noise Reduction
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a comprehensive evaluation using a Z-test to assess the effectiveness of an adaptive Least Mean Squares (LMS) filter driven by the Steepest Descent Method (SDM). The study utilizes a male voice recording, captured in a controlled studio environment, to which persistent Gaussian noise was intentionally introduced, simulating real-world interference. All signal processing methods were implemented accordingly in MATLAB.version: 9.13.0 (R2022b), Natick, MA, USA: The MathWorks Inc.; 2022. The adaptive filter demonstrated a significant improvement of 20 dB in Signal-to-Noise Ratio (SNR) following the initial optimization of the filter parameter μ. To further assess the LMS filter’s performance, an empirical experiment was conducted with 30 young adults, aged between 20 and 30 years, who were tasked with qualitatively distinguishing between the clean and noise-corrupted signals (blind test). The quantitative analysis and statistical evaluation of the participants’ responses revealed that a significant majority, specifically 80%, were able to reliably identify the noise-affected and filtered signals. This outcome highlights the LMS filter’s potential—despite the slow convergence of the SDM—for enhancing signal clarity in noise-contaminated environments, thus validating its practical application in speech processing and noise reduction.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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