Link cancellation waveform, convergence history, and residual spectrum to see how step size trades speed for stability.
Parameters
Target frequency
Hz
Dominant tone to cancel.
Original noise level
dB
Noise level before control.
Adaptation step μ
-
Coefficient update strength.
Filter length
tap
Number of adaptive-filter taps.
Secondary delay
ms
Delay from speaker to error microphone.
Anti-noise convergence animation
—
Residual error e [%]
—
Noise reduction [dB]
0
Iteration n
—
Status
Noise d(n)Anti-noise y(n)Residual e(n)=d−y
Presets:
Convergence history (residual energy)
Against the noise (red), FxLMS learns an inverted anti-noise (blue). Their sum = residual (green) shrinks with each iteration and the error-mic reading drops. Raising μ converges faster, but too large a μ diverges.
FxLMS updates the adaptive filter using a reference signal filtered by the secondary path. A larger step size speeds convergence, but delay and long filters reduce stability margin.
How to read it
The waveform view shows how the anti-noise signal cancels the primary tone.
The convergence plot reveals where a larger step becomes oscillatory.
The spectrum view checks whether the target tone drops without raising nearby bands.
Learn Active Noise Control FXLMS by dialogue
🙋
When reading Active Noise Control FXLMS, where should I look first? Moving Target frequency changes both the plots and the result cards.
🎓
Start with Estimated attenuation, but do not treat the number as the whole answer. Use Cancellation waveform to confirm the assumed state, then read FxLMS convergence for the distribution or trend. The waveform view shows how the anti-noise signal cancels the primary tone.
🙋
I can see why Target frequency changes Estimated attenuation. How should I judge the influence of Original noise level?
🎓
Move Original noise level in small steps and watch Residual noise. That reveals which term is controlling the result. FxLMS updates the adaptive filter using a reference signal filtered by the secondary path. A larger step size speeds convergence, but delay and long filters reduce stability margin. A single operating point is not enough; sweep the realistic scatter range.
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What is Residual spectrum for? It feels like the ordinary curve already tells the story.
🎓
Residual spectrum is for finding boundaries where the condition becomes risky or margin collapses quickly. The convergence plot reveals where a larger step becomes oscillatory. In First-pass studies for duct or tonal machine noise, the important question is often what happens after a small change, not only the nominal value.
🙋
So if Estimated attenuation is within the target, can I accept the condition?
🎓
Treat this as a first-pass review. It helps with Initial tuning of adaptive-filter length and step size and Checking stability margin when secondary-path delay is large, but final decisions still need standards, measured data, detailed analysis, and vendor limits. The spectrum view checks whether the target tone drops without raising nearby bands.
Practical use
First-pass studies for duct or tonal machine noise.
Initial tuning of adaptive-filter length and step size.
Checking stability margin when secondary-path delay is large.
FAQ
Start with Estimated attenuation and Residual noise. Then use Cancellation waveform to confirm the assumed state and FxLMS convergence to read distribution or bias. The waveform view shows how the anti-noise signal cancels the primary tone
Move Target frequency alone, then move Original noise level by a comparable amount and compare the change in Estimated attenuation. Residual spectrum shows combinations where margin or performance changes quickly.
Use it for First-pass studies for duct or tonal machine noise. Instead of trusting a single point, widen the input range and check whether Estimated attenuation keeps enough margin before moving to detailed analysis.
FxLMS updates the adaptive filter using a reference signal filtered by the secondary path. A larger step size speeds convergence, but delay and long filters reduce stability margin. Final decisions still require standards, measured data, detailed analysis, and vendor limits.
How to Use
Set target frequency (50–500 Hz) where noise dominates; use 120 Hz for typical HVAC tonal disturbance
Specify adaptive filter length (taps: 16–256) to match secondary-path delay; 64 taps typical for 5 ms acoustic lag in enclosures
Input noise amplitude (dB re 20 µPa) and observe real-time attenuation, residual noise floor, and stability margin
Worked Example
Active headrest silencing 150 Hz compressor hum: target frequency = 150 Hz, primary noise = 85 dB, step size mu = 0.05, filter taps = 128. Secondary-path delay estimated at 4.2 ms (18 samples @ 4.3 kHz). Simulator predicts 18 dB attenuation after 2.1 seconds, residual noise drops to 67 dB, stability margin = 0.38 (safe operation). Reducing mu to 0.02 extends settling to 4.8 s but boosts margin to 0.61, eliminating instability risk in variable impedance ducting.
Practical Notes
Secondary-path delay dominates stability; underestimating acoustic lag by 2 ms in duct systems triggers oscillation—always verify via impulse response
Mu scaling rule: maximum safe step = 2 / (filter_power × tap_count); exceed this and FxLMS diverges despite convergence theory
Taps efficiency: doubling from 64 to 128 adds ~1.2 dB attenuation but increases computational load 25%; diminishing returns above 200 taps in industrial fans
Frequency bandwidth: single-tone control narrows to ±8 Hz; broadband disturbance (±25 Hz) requires larger filter or cascade controllers