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A graph of the A-, B-, C- and D-weightings across the frequency range 10 Hz – 20 kHz Video illustrating A-weighting by analyzing a sine sweep (contains audio). A-weighting is a form of frequency weighting and the most commonly used of a family of curves defined in the International standard IEC 61672:2003 and various national standards relating to the measurement of sound pressure level. [1]
The weighting curve is specified by both a circuit diagram of a weighting network and a table of amplitude responses. Above is the ITU-R 468 Weighting Filter Circuit Diagram. The source and sink impedances are both 600 ohms (resistive), as shown in the diagram. The values are taken directly from the ITU-R 468 specification.
A commonly used weighting is the A-weighting curve, which results in units of dBA sound pressure level. Because the frequency response of human hearing varies with loudness, the A-weighting curve is correct only at a level of 40- phon and other curves known as B- , C- and D-weighting are also used, the latter being particularly intended for the ...
The ITU-R 468 noise weighting was devised specifically for this purpose, and is widely used in broadcasting, especially in the UK and Europe. A-weighting is also used, especially in the United States, [1] [dubious – discuss] though this is only really valid for the measurement of tones, not noise, and is widely incorporated into sound level ...
A weighting curve is a graph of a set of factors, that are used to 'weight' measured values of a variable according to their importance in relation to some outcome. An important example is frequency weighting in sound level measurement where a specific set of weighting curves known as A-, B-, C-, and D-weighting as defined in IEC 61672 [1] are used.
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Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function.Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time.
Local regression or local polynomial regression, [1] also known as moving regression, [2] is a generalization of the moving average and polynomial regression. [3] Its most common methods, initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing), both pronounced / ˈ l oʊ ɛ s / LOH-ess.