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Matlab Design Digital Filter

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Design and validate digital filters in MATLAB. Use when cleaning up noisy signals, removing interference, filtering signals, designing FIR/IIR filters (lowpass/highpass/bandpass/bandstop/notch), or comparing filters in Filter Analyzer.
— the description by MathWorks

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Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured workflow for designing and validating digital filters within MATLAB. It guides users through signal analysis, architecture selection, and verification, ensuring that filter designs meet specific frequency and magnitude requirements. The skill emphasizes the use of standard MATLAB functions like designfilt and filterAnalyzer, while enforcing best practices for numerical stability and frequency-aware design.

What it does

  • Designs FIR and IIR filters including lowpass, highpass, bandpass, bandstop, and notch types.
  • Provides a systematic workflow for signal analysis and architecture selection.
  • Facilitates visual comparison of filter designs using Filter Analyzer.
  • Enforces best practices for numerical stability, such as using SOS forms for IIR filters.
  • Includes validation steps to verify filter performance against specifications.
  • Supports both streaming and offline filtering implementation strategies.

When to use it

  • When you need to clean up noisy signals or remove specific interference.
  • When designing digital filters with specific frequency and ripple requirements.
  • When comparing different filter architectures like FIR versus IIR.
  • When working on streaming or batch signal processing pipelines.

Advantages

  • Ensures consistent design methodology through a preflight and verification checklist.
  • Promotes numerical stability by recommending specific filter forms.
  • Integrates directly with MATLAB's signal processing and DSP toolboxes.
  • Provides clear guidance on handling narrow transition bands.

Usage examples

“Design a lowpass filter for a 1000 Hz signal with a 200 Hz cutoff.”
The agent will ask for the sample rate (Fs), filter mode (streaming vs offline), and phase requirements before generating the design code.
“How do I compare two different filter designs in MATLAB?”
The agent will guide you to use the filterAnalyzer function and provide the necessary steps to visualize and compare their magnitude and group delay responses.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/matlab/matlab-design-digital-filter/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip matlab-design-digital-filter.zip -d .agents/skills

Documentation by MathWorks

Design Digital Filters in MATLAB

Design, implement, and validate digital filters using Signal Processing Toolbox and DSP System Toolbox. Choose the right architecture (single-stage vs efficient alternatives), generate correct code, and verify the result with plots and numbers.

When to Use

  • Designing lowpass, highpass, bandpass, bandstop, or notch filters

  • Cleaning up noisy signals or removing interference

  • Choosing between FIR and IIR filter architectures

  • Comparing filter designs in Filter Analyzer

  • Building streaming (real-time) or offline (batch) filtering pipelines

  • Handling narrow transition bands with multirate or IFIR approaches

When NOT to Use

  • Adaptive filtering (LMS, RLS) -- use Signal Processing Toolbox docs directly

  • Audio-specific processing (equalization, room correction) -- use Audio Toolbox

  • Image filtering (2D convolution, morphological ops) -- use Image Processing Toolbox

  • General spectral analysis without filtering intent -- FFT/periodogram docs suffice

Key Rules

  • Read references/INDEX.md before writing any filter design code.

  • Always write to .m files. Never put multi-line MATLAB code directly in evaluate_matlab_code. Write to a .m file, run with run_matlab_file, edit on error.

  • Preflight before ANY MATLAB call. Before calling any function listed in INDEX.md, read the required quick-ref first. State Preflight: [files] at the top of the response.

  • Do not guess key requirements. If Mode (streaming vs offline) or Phase requirement is not stated, ask.

  • No Hz designs without Fs. If Fs is unknown, stop and ask (unless the user explicitly wants normalized frequency).

  • Always pin the sample rate. Use designfilt(..., SampleRate=Fs) and freqz(d, [], Fs).

  • IIR stability: Prefer SOS/CTF forms (avoid high-order [b,a] polynomials).

Preflight Procedure

  1. List MATLAB functions to call

  2. Check references/INDEX.md for each (function-level + task-level tables)

  3. Read required quick-ref files

  4. State at response top: Preflight: quick-ref/filter-analyzer.md, quick-ref/designfilt.md (or Preflight: none required)

Workflow

Phase 1: Signal Analysis

  • Analyze input data via MCP (spectrum, signal length, interference location)

  • Compute trans_pct and identify interference characteristics

Phase 2: Clarify Intent

After signal analysis, ask Mode + Phase if not stated:

  • Mode: streaming (causal) | offline (batch)

  • Phase: zero-phase | linear-phase | don't-care

Wait for answer before showing any approach comparison or overview.

Phase 3: Architecture Selection

  • Open references/efficient-filtering.md if trans_pct < 2%

  • Show only viable candidates given Mode + Phase constraints

  • Explicitly state excluded families with one-line reason

  • Use Filter Analyzer for visual comparison

Design Intake Checklist

Required signal + frequency spec (cannot proceed without)

  • Fs (Hz)

  • Response type: lowpass / highpass / bandpass / bandstop / notch

  • Edge frequencies in Hz

If any item is missing, ask.

Required intent for architecture choice (ask if unknown)

  • Mode: streaming (causal) | offline (batch)

  • Phase: zero-phase | linear-phase | don't-care

  • Magnitude constraints: Rp_dB passband ripple (default 1 dB), Rs_dB stopband attenuation (default 60 dB)

If Mode or Phase is unknown, ask 1-2 clarifying questions and stop.

Architecture Checkpoint

Compute and state before finalizing an approach:

  • trans_bw = Fstop - Fpass

  • trans_pct = 100 * trans_bw / Fs

  • M_max = floor(Fs/(2*Fstop)) (only meaningful for lowpass-based multirate)

Decision rule:

  • trans_pct > 5% -- single-stage FIR or IIR is usually fine

  • 2% <= trans_pct <= 5% -- single-stage possible; mention efficient alternatives if cost/latency matters

  • trans_pct < 2% -- stop and do a narrow-transition comparison (see references/quick-ref/efficient-filtering.md)

Design + Verify

  1. Feasibility check -- Let designfilt choose minimum order, then query filtord(d). Optionally use kaiserord/firpmord for FIR length estimates.

  2. Design candidates -- Prefer designfilt() with explicit Rp/Rs and SampleRate=Fs. Streaming IIR: use SystemObject=true. Offline zero-phase: filtfilt() is allowed but state that it squares the magnitude response.

  3. Compare visually -- Use filterAnalyzer() for comparing 2+ designs. Read references/quick-ref/filter-analyzer.md first. Minimum displays: magnitude + group delay.

  4. Verify with numbers -- Worst-case passband ripple and stopband attenuation vs spec. For filtfilt(), verify the effective response (magnitude squared).

  5. Deliver the output -- Specs recap, derived metrics, chosen architecture + why, MATLAB code, verification snippet + results, implementation form.

Key Functions

FunctionPurpose
designfilt()Primary filter design (FIR and IIR, all response types)
filterAnalyzer()Visual comparison of 2+ filter designs
freqz(), grpdelay()Frequency response and group delay analysis
filtfilt()Zero-phase offline filtering
dsp.SOSFilterStreaming IIR via SystemObject=true
designMultirateFIR()Multirate decimator/interpolator design
ifir()Interpolated FIR for narrow transitions at constant rate
cost()MPIS (MultiplicationsPerInputSample) on DSP System objects
kaiserord(), firpmord()FIR order estimation

Conventions

  • Always specify SampleRate=Fs in designfilt() and plot in Hz with freqz(d, [], Fs)

  • Use filterAnalyzer() for multi-filter comparison, not custom freqz/grpdelay plots

  • Use SOS form for IIR (avoid [b,a] for order > 8)

  • Use tiledlayout/nexttile for multi-panel figures (not subplot)

Copyright 2026 The MathWorks, Inc.

Files

references/multirate.md

Multirate Filtering Guide

Complete guide for multirate filtering in MATLAB. Covers when to use each function, streaming vs offline workflows, and cost analysis.

Table of Contents

  • Critical Decision: Which Function to Use?

  • When Not to Use designMultistageDecimator/Interpolator

  • Concept: Multirate Pipeline

  • Workflow 1: Streaming Multirate

  • Workflow 2: Offline Zero-Phase

  • Workflow 3: Wideband Rate Conversion

  • Workflow 4: Complex Specifications

  • Cost Analysis

  • Key Functions Reference

  • Gotchas

  • API References

  • Visualization

Critical Decision: Which Function to Use?

The choice of multirate function depends on your use case. Using the wrong function can result in 5× worse performance.

Quick Decision Table
Use CaseRecommended ApproachWhy
Narrow transition + streamingdesignMultirateFIR + separate sharp filterRelaxed anti-alias + efficient sharp filter at low rate
Narrow transition + offlineresample() + sharp filter + filtfilt()Simplest, built-in anti-alias, zero-phase
Wideband rate change (M≥8)designMultistageDecimator/InterpolatorAutomatic cascade optimization
Complex frequency specsfdesign.decimator + design()Flexible specification object
CIC or halfband filtersfdesign.decimator with CIC responseSpecialized filter types

IMPORTANT: When NOT to Use designMultistageDecimator/Interpolator

These functions are NOT optimal for narrow-transition filtering!

When you pass tight specs (narrow TW, high Astop), these functions try to achieve the sharp transition within the decimation/interpolation, requiring very long filters.

Real Example (Fs=44.1kHz, Fpass=2.8kHz, Fstop=3.1kHz, 60dB)
ApproachMPISResult
designMultirateFIR + sharp filter115.75BEST
Single-stage FIR407.00Baseline
designMultistageDecimator + Interpolator with tight specs531.25WORSE!

Lesson: For narrow transitions, use designMultirateFIR (relaxed anti-alias) + separate sharp filter at reduced rate.

When designMultistage* IS Optimal

For wideband rate conversion (just anti-alias, no specific filtering), designMultistageDecimator/Interpolator with default specs is very efficient:

MdesignMultirateFIR (MPIS)designMultistage* (MPIS)Winner
490.260.8Multistage
8189.181.9Multistage (2.3×)
16382.6128.4Multistage (3.0×)
32767.3190.7Multistage (4.0×)

Concept: Multirate Pipeline

Input (Fs) → Decimate (÷M) → Filter (Fs/M) → Interpolate (×M) → Output (Fs)

Why it works: A 400-tap filter at 44.1 kHz becomes a ~100-tap filter at 11 kHz (after 4× decimation), and processes 1/4 as many samples.

Output rate = Input rate: Decimation and interpolation cancel out.

Workflow 1: Streaming Multirate (Narrow Transition)

Use designMultirateFIR for anti-alias + separate sharp filter.

%% Streaming multirate with narrow transition band
Fs = 44100;
Fpass = 2800; Fstop = 3100;
Rp = 0.1; Rs = 60;
M = 4;  % Decimation/interpolation factor
Fs_dec = Fs / M;

%% Design filters
% Step 1: Decimator with RELAXED anti-alias (just prevent aliasing)
dec = designMultirateFIR(DecimationFactor=M, ...
    StopbandAttenuation=Rs, SystemObject=true);

% Step 2: Sharp lowpass at reduced rate
d_sharp = designfilt("lowpassfir", ...
    PassbandFrequency=Fpass, StopbandFrequency=Fstop, ...
    PassbandRipple=Rp, StopbandAttenuation=Rs, ...
    SampleRate=Fs_dec, DesignMethod="equiripple");

% Step 3: Interpolator with anti-image
interp = designMultirateFIR(InterpolationFactor=M, ...
    StopbandAttenuation=Rs, SystemObject=true);

%% Process frames (streaming)
frameSize = 1024;  % Must be multiple of M
y = zeros(size(x));

for i = 1:frameSize:length(x)-frameSize+1
    frame = x(i:i+frameSize-1);

    % Pipeline: decimate → sharp filter → interpolate
    frame_dec = dec(frame);
    frame_filt = filter(d_sharp, frame_dec);  % Causal
    frame_out = interp(frame_filt);

    y(i:i+frameSize-1) = frame_out;
end

Note: Streaming is causal only — no filtfilt(). There will be group delay.

Workflow 2: Offline Zero-Phase (Narrow Transition)

Use resample() for simplicity — it has built-in anti-alias/anti-image filters.

%% Offline multirate with zero-phase sharp filter
Fs = 44100;
Fpass = 2800; Fstop = 3100;
Rp = 0.1; Rs = 60;
M = 4;
Fs_dec = Fs / M;

% Load signal
[x, ~] = audioread("noisy_signal.wav");

%% Stage 1: Decimate (resample handles anti-aliasing)
x_dec = resample(x, 1, M);

%% Stage 2: Sharp lowpass at reduced rate (zero-phase)
d_sharp = designfilt("lowpassfir", ...
    PassbandFrequency=Fpass, StopbandFrequency=Fstop, ...
    PassbandRipple=Rp, StopbandAttenuation=Rs, ...
    SampleRate=Fs_dec, DesignMethod="equiripple");

x_filt = filtfilt(d_sharp, x_dec);  % Zero-phase at low rate

%% Stage 3: Interpolate back (resample handles anti-imaging)
y = resample(x_filt, M, 1);

% Output y is at original Fs, zero-phase filtered

Advantages:

  • Simple 3-line pipeline

  • resample() handles anti-alias/anti-image automatically

  • filtfilt() gives zero-phase at reduced computational cost

Workflow 3: Wideband Rate Conversion (Large M)

Use designMultistageDecimator/Interpolator with DEFAULT specs for pure rate change.

%% Wideband rate conversion (M=16, no specific filtering)
M = 16;
Fs = 48000;

% Default specs - optimized for wideband anti-alias
dec = designMultistageDecimator(M);
interp = designMultistageInterpolator(M);

% View cascade structure
info(dec)
info(interp)

% Cost comparison
cost_dec = cost(dec);
cost_interp = cost(interp);
fprintf('Decimator: %.2f MPIS\n', cost_dec.MultiplicationsPerInputSample);
fprintf('Interpolator: %.2f MPIS\n', cost_interp.MultiplicationsPerInputSample);

% Apply (streaming)
y_dec = dec(x);
y_out = interp(y_dec);

When to use: Decimation factor M > 8, or when you want MATLAB to optimize the cascade automatically.

Workflow 4: Complex Specifications (fdesign)

Use fdesign.decimator when you need specific passband/stopband control.

%% fdesign.decimator with lowpass specification
M = 4;
Fs = 44100;
Fpass_norm = 2800 / Fs;  % Normalized to Fs
Fstop_norm = 3100 / Fs;
Rp = 0.1; Rs = 60;

% Create specification object
d = fdesign.decimator(M, 'lowpass', 'Fp,Fst,Ap,Ast', ...
    Fpass_norm, Fstop_norm, Rp, Rs);

% Available design methods
designmethods(d)

% Design with kaiserwin (handles tight specs)
hd = design(d, 'kaiserwin', SystemObject=true);
cost(hd)

% Design with multistage (deprecated - use designMultistageDecimator)
% hd_multi = design(d, 'multistage', SystemObject=true);

Key points:

  • Frequencies are normalized to Fs (0 to 1)

  • Use 'kaiserwin' for tight specs where 'equiripple' fails

  • The 'multistage' method is deprecated — use designMultistageDecimator instead

Cost Analysis: How to Compare Approaches

Using the cost() Function
%% Measure MPIS for each component
M = 4;
Rs = 60;

% Decimator
dec = designMultirateFIR(DecimationFactor=M, ...
    StopbandAttenuation=Rs, SystemObject=true);
cost_dec = cost(dec);
fprintf('Decimator: %d taps, %.2f MPIS\n', ...
    length(dec.Numerator), cost_dec.MultiplicationsPerInputSample);

% Sharp filter at reduced rate
d_sharp = designfilt("lowpassfir", ...
    PassbandFrequency=2800, StopbandFrequency=3100, ...
    StopbandAttenuation=Rs, SampleRate=44100/M, ...
    DesignMethod="equiripple");
sharp_sys = dsp.FIRFilter(Numerator=d_sharp.Numerator);
cost_sharp = cost(sharp_sys);
fprintf('Sharp filter: %d taps, %.2f MPIS (at 1/%d rate = %.2f effective)\n', ...
    length(d_sharp.Numerator), cost_sharp.MultiplicationsPerInputSample, ...
    M, cost_sharp.MultiplicationsPerInputSample/M);

% Interpolator
interp = designMultirateFIR(InterpolationFactor=M, ...
    StopbandAttenuation=Rs, SystemObject=true);
cost_interp = cost(interp);
fprintf('Interpolator: %d taps, %.2f MPIS\n', ...
    length(interp.Numerator), cost_interp.MultiplicationsPerInputSample);

% Total pipeline cost
total_mpis = cost_dec.MultiplicationsPerInputSample + ...
             cost_sharp.MultiplicationsPerInputSample/M + ...
             cost_interp.MultiplicationsPerInputSample;
fprintf('TOTAL: %.2f MPIS\n', total_mpis);
Comparing with resample() Pipeline

resample() has internal filters not exposed to cost(). Use timeit():

x = randn(44100*2, 1);  % 2 seconds at 44.1 kHz

f_pipeline = @() resample(filtfilt(d_sharp, resample(x, 1, M)), M, 1);
t_pipeline = timeit(f_pipeline);
fprintf('Offline pipeline: %.2f ms\n', t_pipeline * 1000);

Key Functions Reference

FunctionUse CaseReturns
designMultirateFIR()Single-stage decimator/interpolatorCoefficients or System object
designMultistageDecimator()Optimal cascaded decimator (large M, wideband)dsp.FilterCascade
designMultistageInterpolator()Optimal cascaded interpolator (large M, wideband)dsp.FilterCascade
resample(x, P, Q)Offline rate conversion with anti-aliasResampled signal
fdesign.decimator()Flexible decimator specificationSpecification object
fdesign.interpolator()Flexible interpolator specificationSpecification object
dsp.FIRDecimatorStreaming decimation System objectSystem object
dsp.FIRInterpolatorStreaming interpolation System objectSystem object

Gotchas

1. Don't Use designMultistage* with Tight Specs for Narrow Transitions
% Template
% WRONG: Trying to do sharp filtering within decimation
dec = designMultistageDecimator(M, Fs, TW, Rs);  % TW = narrow

% RIGHT: Relaxed decimator + separate sharp filter
dec = designMultirateFIR(DecimationFactor=M, ...);
d_sharp = designfilt("lowpassfir", ..., SampleRate=Fs/M);
2. Don't Use System Objects with filtfilt()
% Anti-pattern
% WRONG: System objects have internal state
dec_sys = designMultirateFIR(DecimationFactor=M, SystemObject=true);
y = filtfilt(dec_sys, x);  % ERROR!

% RIGHT: Use resample() for offline zero-phase
x_dec = resample(x, 1, M);
3. Frame Size Must Be Multiple of M
% For streaming decimation
frameSize = 1024;  % OK for M=4 (1024/4 = 256)
frameSize = 1000;  % ERROR for M=4 (not divisible)
4. resample() Has Hidden Filter Cost

Don't claim "100 taps at 11 kHz" — resample() adds its own anti-alias/anti-image filtering. Measure total pipeline cost with timeit().

5. Fs Meaning Differs Between Functions

  • designMultistageDecimator(M, Fs, ...): Fs = input rate

  • designMultistageInterpolator(L, Fs, ...): Fs = output rate (R2024b+)

API References

Doc FileContent

Visualization

All multirate filters work with Filter Analyzer for response visualization and comparison.

Visualizing Individual Multirate Filters
% Compare decimator designs in Filter Analyzer
dec4 = designMultirateFIR(DecimationFactor=4, SystemObject=true);
decMulti = designMultistageDecimator(8);
filterAnalyzer(dec4, decMulti, SampleRates=Fs, ...
    FilterNames=["SingleStage", "Multistage"]);
Visualizing Complete Multirate Pipelines

IMPORTANT: For comparing dec→filter→interp pipelines against single-stage filters, wrap the complete pipeline in dsp.FilterCascade:

%% Create cascade from multirate pipeline for Filter Analyzer
Fs = 44100; M = 4;

% Build pipeline components
dec_sys = designMultirateFIR(DecimationFactor=M, StopbandAttenuation=60, SystemObject=true);
d_core = designfilt("lowpassfir", ...
    PassbandFrequency=2800, StopbandFrequency=3000, ...
    StopbandAttenuation=60, SampleRate=Fs/M, DesignMethod="equiripple");
core_sys = dsp.FIRFilter('Numerator', d_core.Numerator);  % Wrap digitalFilter!
interp_sys = designMultirateFIR(InterpolationFactor=M, StopbandAttenuation=60, SystemObject=true);

% Create cascade for visualization
multirate_cascade = dsp.FilterCascade(dec_sys, core_sys, interp_sys);

% Add to Filter Analyzer alongside other filter types
filterAnalyzer(multirate_cascade, SampleRates=Fs, FilterNames="Multirate_Pipeline");

Key points:

  • Wrap digitalFilter in dsp.FIRFilter before adding to cascade

  • dsp.FilterCascade shows the overall response of the pipeline

  • Use same SampleRates=Fs as the input rate

See filter-analyzer.md → "Visualizing Multirate Pipelines" for complete comparison examples.

See Also

  • efficient-filtering.md — Decision guide for choosing approach

  • multistage-ifir.md — IFIR (constant-rate alternative)

  • patterns.md — Code templates

  • filter-analyzer.md — Multirate filter visualization

Copyright 2026 The MathWorks, Inc.

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Created by
MathWorks
Publisher
MathWorks (Official)
Source
matlab/matlab-agentic-toolkit · skills-catalog/signal-processing/matlab-design-digital-filter · commit 2a7f215
Licence
BSD 2-Clause License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE.md)
  • ATTRIBUTION.md (written by AutoGineer)
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