`AdaptiveFilters.NLMS`

— Method`NLMS(n::Int, μ::T)`

Create an `NLMS`

FIR-filter with `n`

coefficients (filter taps) and learning rate `0 < μ ≤ 1`

. The type of `μ`

determines the numeric type used by the filter.

Call the filter object like a function `ŷ, e = f(x, d)`

where `x`

is the input and `d`

is the desired output. To create an adaptive line enhancer (ALE), set $d[k] = x[k-Δ]$ where $Δ$ is a positive integer delay, like 1.

`AdaptiveFilters.adaptive_filter`

— Function`yh = adaptive_filter(y, alg=MSPI; order=4, lr=0.1)`

Filters `y`

with an adaptive AR (only poles) filter with specified order. Returns `yh`

which is the predicted output from an adaptive line enhancer (ALE). If your noise is wideband and signal narrowband, `yh`

is your desired filtered signal. If the noise is narrowband and the signal is wideband, then `y-yh`

is your desired filtered signal.

The first `order`

samples of `yh`

will be copies of `y`

. The signals will thus have the same length.

#Arguments:

`alg`

: Stochastic approximation algorithm or weight function. Examples:`OMAP, MSPI, OMAS, ADAM, ExponentialWeight, EqualWeight`

`y`

: Input signal`order`

: Filter order`lr`

: Learning rate or weight depending on`alg`

`AdaptiveFilters.focused_adaptive_filter`

— Method`focused_adaptive_filter(y, band, fs, args...; kwargs...)`

An adaptive filter that focuses its attention to a specific frequency range.

#Arguments:

`y`

: Input signal`band`

: Frequency band typle`fs`

: Sample rate, e.g., 44100`args`

: Passed through to`adaptive_filter`

`kwargs`

: Passed through to`adaptive_filter`