Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Шкала Барка и шкала Мел× | Активное шумоподавление с использованием алгоритма FxLMS× | |
|---|---|---|
| Область | Акустика | Акустика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1937 | 1975 |
| Автор метода≠ | Eberhard Zwicker, Stanley Smith Stevens | Bernard Widrow, Samuel Stearns |
| Тип≠ | Perceptual frequency mapping | Adaptive noise cancellation algorithm |
| Основополагающий источник≠ | Zwicker, E. (1961). Subdivision of the audible frequency range into critical bands. Journal of the Acoustical Society of America, 33(2), 248–248. link ↗ | 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. DOI ↗ |
| Другие названия | bark scale, mel scale, critical bandwidth, perceptual frequency | FxLMS, filtered-x LMS, active noise cancellation, ANC |
| Связанные | 5 | 5 |
| Сводка≠ | Bark and Mel scales are perceptual frequency scales that map physical frequency (Hz) to perceived pitch and auditory perception. Formalized by Zwicker (Bark, 1961) and Stevens (Mel, 1937), these non-linear scales reflect how the human ear processes sound. Bark scale divides hearing into 24 critical bands; Mel scale models pitch perception. Both are essential for audio feature extraction, speech processing, and designing audio systems that align with human hearing. | 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. |
| ScholarGateНабор данных ↗ |
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