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	Algorytm FxLMS (Filtered-x Least Mean Squares) do aktywnej kontroli hałasu	Analiza cepstralna
Dziedzina	Akustyka	Akustyka
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1975	1963
Twórca	Bernard Widrow, Samuel Stearns	Bogert, Healy, Tukey
Typ	Adaptive noise cancellation algorithm	Spectral decomposition method
Źródło pierwotne	Widrow, B., & Stearns, S. D. (1975). Adaptive signal processing for active vibration and noise control. IEEE Transactions on Acoustics, Speech, and Signal Processing, 23(5), 440–453.	Bogert, B. P., Healy, M. J., & Tukey, J. W. (1963). The quefrency alanysis of time series for echoes: cepstrum, pseudo-autocovariance, cross-cepstrum, and saphe cracking. In Time Series Analysis Research Papers (pp. 209–243). Wiley.
Inne nazwy	FxLMS, filtered-x LMS, active noise cancellation, ANC	cepstrum, MFCC, mel-frequency cepstral coefficients, spectral analysis
Pokrewne	5	5
Podsumowanie	The Filtered-x Least Mean Squares (FxLMS) algorithm is an adaptive filter used in active noise control (ANC) systems to reduce unwanted sound by generating anti-noise. Pioneered by Widrow and Stearns in 1975 and refined by Eriksson and colleagues, FxLMS is the most widely deployed algorithm in commercial noise-canceling headphones, hearing aids, automotive cabins, and industrial noise barriers. It works by continuously learning the acoustical path and dynamically adjusting a canceling signal in real time.	Cepstral analysis is a spectral analysis technique that decomposes signals into independent components by inverting the log-magnitude spectrum. Pioneered by Bogert, Healy, and Tukey in 1963, cepstral analysis reveals periodic structure in spectra (pitch, echo patterns) and separates source excitation from filter response. Mel-frequency cepstral coefficients (MFCCs) derived from cepstral analysis are the most widely used features in automatic speech recognition, speaker verification, and audio analysis.