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The Normalized Least Mean Squares (NLMS) adaptive filter is an FIR based adaptive filter. The filter update equation is given by
w(n+1) = w(n) +[(μ * e(n) * x(n)) / Eng] \\
Where:
w(n+1) = New filter coefficient set w(n) = Current filter coefficient set μ = Learning rate x(n) = Input signal e(n) = Error = d(n)-y(n) d(n) = Desired signal y(n) = Output signal Eng = Average energy over the duration of the filter = (1/M)*Σx(m) <sup>2</sup>
Name | Format [int/dec/float] - [control/audio] | Function Description |
---|---|---|
Pin 0: Input | Float- audio | Input signal to FIR filter |
Pin 1: Input | Float- audio | The desired audio output signal |
Pin 2: Input | Float- control | Signal which indicates whether FIR filter weights are to be updated or not |
Name | Format [int/dec/float] - [control/audio] | Function Description |
---|---|---|
Pin 0: Output | Float - audio | The filtered output |
Pin 1: Output | Float - control | Error Signal - Difference signal between desired signal and the filtered output |
The module does not support growth and add functionality.
GUI Control Name | Default Value | Range | Function Description |
---|---|---|---|
Alpha | 0.1 | 0.000001-1.0 in steps of 0.000001 | Filter learning rate |
GUI Control Name | Compiler Name | Function Description |
---|---|---|
Alpha | NLMSFiltBlkAlg1lms_alpha | Filter learning rate |
Here,
This implementation of NLMS is a block based implementation. The weight update happens at the end of every processing block. The weight update equation is given below:
w(m+1) = w(m) + μ * Err * x(m) / Eng
Where:
w(m+1) = New filter coefficient set w(m) = Current filter coefficient set μ = Learning rate Err = RMS error over the block duration = sqrt(∑e(n)2/N) e(n) = d(n)-y(n) d(n) = Desired signal y(n) = Output signal x(m) = Samples within the filter state Eng = Average energy over the duration of the filter = Σx(m)2/M
Note:
N = Block Size and n varies over 0-(N-1) M = Filter tap length and m varies over 0-(M-1)
1. ADSP-213xx
2. ADSP-214xx
3. ADSP-215xx
4. ADSP-SC5xx