Matlab Prepare Signal Data

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From the author

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

— the description by MathWorks

AutoGineer summary

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

This skill provides a structured approach to signal data preparation, guiding users through the lifecycle of cleaning raw signals, managing datastores, applying labels, and partitioning data for machine learning. It emphasizes using high-level Signal Processing Toolbox functions to ensure readability and maintainability, while offering clear heuristics for ordering conditioning steps like detrending, smoothing, and outlier removal. The skill is designed to help users move from raw data to a ready-to-train state for models like trainnet.

What it does

Cleans signals by filling gaps, removing drift, denoising, and handling outliers.
Synchronizes and resamples multi-channel data onto a uniform time base.
Builds data pipelines using signalDatastore for efficient loading.
Creates labeledSignalSet objects for structured labeling and Signal Labeler interoperability.
Performs stratified splits and framing for long signal sequences.
Shapes and prepares datastore outputs for machine learning workflows.

When to use it

When you need to clean a single signal before analysis or training.
When preparing a dataset of signals for machine learning with specific labeling requirements.
When you need to align multiple signal channels or resample them to a uniform rate.
When you need to partition signal data into training, validation, and test sets.
When you need to frame long signals for windowed analysis or supervision.

Advantages

Promotes readable, maintainable code by preferring high-level toolbox functions.
Provides a logical, verified order for conditioning steps to prevent data corruption.
Reduces manual effort by automating common tasks like label derivation from filenames.
Ensures compatibility with Signal Labeler and deep learning training functions.
Offers clear guidance on when to escalate to lower-level methods.

Usage examples

“How do I remove a linear trend from my signal?”: The agent will recommend using detrend(x, 1) and explain why explicit degree specification is preferred for code clarity.
“I have a folder of signal files; how do I create a datastore and split them for training?”: The agent will guide you through using signalDatastore to load the files, followed by splitlabels to perform a stratified split.
“My signal has gaps; how should I fix them?”: The agent will suggest using fillmissing for short gaps or fillgaps for longer gaps in oscillatory signals, depending on the signal characteristics.
“How do I align two signals with different sampling rates?”: The agent will recommend using the synchronize function to place both signals onto a shared time grid.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/matlab/matlab-prepare-signal-data/download

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

mkdir -p .agents/skills && unzip matlab-prepare-signal-data.zip -d .agents/skills

Documentation by MathWorks

Prepare Signal Data

Look in Signal Processing Toolbox first. The conditioning, labeling, splitting, framing, and partitioning helpers here live in Signal Processing Toolbox — not in Stats & ML Toolbox or generic-MATLAB string utilities.

The arc: condition a raw signal (clean it) -> load a folder into a datastore -> label -> split / frame -> hand off to trainnet. Each stage is a workflow file; this page routes you to the right one.

When to Use

Cleaning a single signal before analysis: fill gaps, remove drift, deoutlier, denoise, put it on a uniform time base, align multiple channels.
Loading / preparing signal data for ML training: datastores, labels from filenames or folders, stratified splits, framing, parallel processing.
Structured labeling: labeledSignalSet for Signal Labeler, all label types.

When NOT to Use

Raw .wav audio classification with Audio Toolbox available. audioDatastore is the canonical path (this skill's custom-ReadFcn workflow handles .wav only when Audio Toolbox is absent — references/wf-custom-readfcn.md).
Frequency-selective filter DESIGN (band isolation, notch, custom FIR/IIR) — see the matlab-design-digital-filter skill. This skill's conditioning is about cleaning, not designing filters.
Computing per-frame features (RMS, crest factor, spectral / bandwidth, time-frequency features) from an already-conditioned signal — see the matlab-extract-signal-features skill. This skill's framesig / framelbl are for manual per-window labeling / supervision, not for deriving a feature table; the signal*FeatureExtractor objects window internally and emit the table.

Best practices

Deliverable is a runnable .m script the user can save, version, and re-run — not workspace state.
Prefer the highest-level function that does the job. detrend / smoothdata / fillmissing / resample read cleanly and are easy for a non-expert to follow. Drop to a lower-level / more-configurable path (designfilt + filtfilt, a hand-built AR model, a named primitive) only when you need control the high-level call cannot give, or when the user asks. Readability first; escalate to low-level for necessity, not by default.
- The high-level call usually exposes the control you think you need. In particular smoothdata(x, "sgolay", fl) takes the frame length fl as an argument — it does NOT hide it — so prefer it over calling sgolayfilt directly. Reach for sgolayfilt only for what the dispatcher genuinely lacks (derivative output via dn, or an unusual polynomial order).

0. Common reflexes

If your first instinct is one of these, the canonical replacement is one row away.

Reflex	Canonical	Detail
Hand-design a highpass/`designfilt` to remove a smooth drift	`detrend(x, n)` — escalate `n` = 1 -> 2 -> 3 before reaching for a filter; polynomial detrend has unity passband gain	references/fn-detrend.md
Invent a gap-filler (`regularizeNaNs`, `inpaintn` — not real)	`fillmissing` (interp) for short gaps; `fillgaps` (SPT, AR) for long gaps in oscillatory signals	references/wf-repair-missing.md
Hand-roll `retime` + shift + `retime` + concat to align channels	`synchronize(A, B, ...)` — one call to a shared grid	references/wf-align-channels.md
Custom `ReadFcn` for a `.csv`	`signalDatastore` default reader + `SignalVariableNames`	references/fn-signaldatastore.md
`cvpartition` for a datastore split	`splitlabels` + `subset(ds, idx{k})`	references/fn-splitlabels.md
`regexp` / `extractBefore` / `fileparts` for labels from filenames	`filenames2labels(sds, Extract=...)`	references/fn-filenames2labels.md
`regexp` / nested `fileparts` for labels from subfolders	`folders2labels(sds.Files)`	references/fn-folders2labels.md
Manual framing loop with `(i-1)*hop+1`	`framesig(x, fl, OverlapLength=...)`	references/wf-frame-and-label.md
Manual ROI-to-frame vote with `containers.Map`	`framelbl(rois, ...)`	references/wf-frame-and-label.md
`for` loop `load(file)` to read in-file label variables	`signalDatastore(folder, SignalVariableNames=["x","label"])`	references/fn-signaldatastore.md
`signalMask` when you need Signal Labeler interop	`labeledSignalSet` with ROI labels (signalMask can't import)	references/fn-labeledsignalset.md
`signalLabeler(lss)` (pass the set as an arg)	Launch bare `signalLabeler` (zero args), then Import -> From Workspace or From File	references/wf-label-and-export.md

SPT-specialized functions exist — reach for them, don't reinvent. fillgaps (AR gap fill), medfilt1 / hampel (impulse handling), sgolayfilt / smoothdata(...,"sgolay") (feature-preserving smoothing) are in Signal Processing Toolbox.

1. Workflows

Each workflow file is the entry point and lists the functions it uses. Start here.

Workflow	Use when	Reference
Repair missing samples	NaN gaps / dropouts to fill.	references/wf-repair-missing.md
Detrend, smooth, deoutlier	Drift, spikes, and/or broadband noise on one signal (smoothing/denoising lives here).	references/wf-detrend-smooth-deoutlier.md
Align multi-rate / offset channels	Several channels onto a shared time base.	references/wf-align-channels.md
Put one channel on a uniform rate	One channel -> uniform grid at a chosen rate: jittery timestamps to regularize, OR already uniform but the wrong rate to `resample`.	references/wf-uniform-rate.md
Wavelet denoising (escalation)	Non-stationary/multi-scale noise a tuned `sgolayfilt` can't remove; `wdenoise` (Wavelet TB).	references/wf-denoise.md
Envelope extraction	Amplitude outline (AM demod, peak hull) — not cleaning.	references/wf-envelope.md
Load + label + split	Folder of files -> datastore for training.	references/wf-load-and-split.md
Frame long signals + per-frame labels	Long signals, per-window supervision.	references/wf-frame-and-label.md
Label + export (all label types)	Structured labels (attribute/ROI/point/TF-ROI), export to Signal Labeler / DL.	references/wf-label-and-export.md
Parallel processing across a parpool	Per-signal work across workers.	references/wf-parallel-process.md
Custom ReadFcn (only when needed)	Format isn't `.mat` / `.csv`, or has a metadata prelude.	references/wf-custom-readfcn.md
Hand-off to `trainnet`	Datastore ready; shape for `trainnet` / `combine`.	references/wf-handoff-to-dl.md

Each workflow file names the fn- reference pages for the functions it uses; there is no separate function index — enter through the workflow that matches your task, or the reflex table above.

2. Ordering when a signal needs several conditioning steps

The governing principle (this is the real rule): order the steps so an earlier operation does not corrupt the input to a later one. Spikes bias least-squares fits and get smeared by filters/resamplers; an un-removed trend gets averaged into the signal by a smoother; most operations choke on NaN. Reason from that for the signal in front of you — do not follow a fixed chain blindly.

Default heuristic (a good starting order, not a universal law):

outliers -> detrend -> smooth, with fill and align placed by the principle above.

outliers -> detrend -> smooth is the verified core: remove spikes before a polynomial detrend (a spike biases the fit) and before a smoother (a smoother spreads the spike across its window); detrend before smooth so the smoother isn't averaging across a trend.
Fill NaN before any step that can't handle missing data (detrend, filters, most smoothers).
Align / resample: putting a signal on a new grid (retime/synchronize) creates NaN at non-overlapping times, so fill after aligning. BUT if the signal has spikes, deoutlier before resampling — resample's anti-alias filter will smear an un-removed spike. So align-vs-outliers order depends on the signal; the principle decides, not a fixed sequence.

Not every signal needs every step — identify which apply, order them by the principle, and each workflow file has an off-ramp if your problem is actually a different family.

Files

ATTRIBUTION.mdAdded by AutoGineer1.0 KiB
LICENSELicence file added by AutoGineer1.4 KiB
SKILL.md9.5 KiB
manifest.yaml650 B
references/fn-countlabels.md2.1 KiB
references/fn-detrend.md4.6 KiB
references/fn-filenames2labels.md4.8 KiB
references/fn-fillgaps.md3.3 KiB
references/fn-fillmissing.md4.2 KiB
references/fn-filloutliers.md5.0 KiB
references/fn-folders2labels.md3.4 KiB
references/fn-framelbl.md7.1 KiB
references/fn-framesig.md4.1 KiB
references/fn-hampel.md3.3 KiB
references/fn-labeledsignalset.md9.1 KiB
references/fn-labelspectrogramoptions.md4.6 KiB
references/fn-medfilt1.md3.3 KiB
references/fn-partition.md4.6 KiB
references/fn-resample.md4.6 KiB
references/fn-retime.md4.7 KiB
references/fn-sgolayfilt.md3.5 KiB
references/fn-signaldatastore.md11.9 KiB
references/fn-signallabeldefinition.md5.9 KiB
references/fn-signalmask-getmask.md8.6 KiB
references/fn-smoothdata.md4.8 KiB
references/fn-splitlabels.md5.3 KiB
references/fn-subset.md3.9 KiB
references/fn-synchronize.md4.7 KiB
references/fn-wdenoise.md5.2 KiB
references/wf-align-channels.md6.6 KiB
references/wf-custom-readfcn.md5.0 KiB
references/wf-denoise.md2.6 KiB
references/wf-detrend-smooth-deoutlier.md6.6 KiB
references/wf-envelope.md5.3 KiB
references/wf-frame-and-label.md4.7 KiB
references/wf-handoff-to-dl.md9.1 KiB
references/wf-label-and-export.md8.7 KiB
references/wf-load-and-split.md4.6 KiB
references/wf-parallel-process.md4.8 KiB
references/wf-repair-missing.md6.7 KiB
references/wf-uniform-rate.md4.2 KiB

references/fn-signalmask-getmask.md

Functions: `signalMask` + `catmask` / `binmask` / `roimask`

Used in: wf-frame-and-label.md Toolbox: Signal Processing Toolbox

Build a per-sample categorical mask from an ROI table, a categorical sequence, or a matrix of binary sequences. Use when supervision is per-sample (not per-frame) — for example, a dense segmentation network that emits one label per sample.

Signatures

msk     = signalMask(src)                                  % construct (any source form)
msk     = signalMask(src, Name=Value, ...)                 % with property NV-pairs

seq     = catmask(msk)                                     % categorical seq, source-length default
seq     = catmask(msk, len)                                % categorical seq of length len
seq     = catmask(___, OverlapAction=action)               % overlap policy (default 'error')
seq     = catmask(___, OverlapAction="prioritizeByList", PriorityList=idxlist)
[seq, numroi, cats] = catmask(___)

seqs    = binmask(msk)                                     % matrix of binary sequences
seqs    = binmask(msk, len)
[seqs, numroi, cats] = binmask(___)

tbl     = roimask(msk)                                     % round-trip to ROI table
[tbl, numroi, cats]  = roimask(msk)

slices  = extractsigroi(msk, signal)                       % per-region signal slices
plotsigroi(msk, signal)                                    % plot signal with colored ROIs

When SourceType is 'roiTable', len is required for catmask / binmask. When SourceType is 'categoricalSequence' or 'binarySequences', omitting len defaults to the source's own length.

Input forms (3)

src accepts three forms; the chosen form is recorded in the read-only SourceType property.

Form	Shape	Example	`SourceType`
ROI table	Table whose first variable is an Mx2 numeric matrix of `[start end]` limits and second variable is a categorical or string array of labels. Variable names are not required to be `ROILimits` / `Value`.	`signalMask(table([2 4;6 7], ["male" "female"]'))`	`"roiTable"`
Categorical vector sequence	Categorical vector; contiguous same-value runs become regions; `<undefined>` marks gaps between regions.	`signalMask(categorical(["" "A" "A" "" "B"]'))`	`"categoricalSequence"`
Binary-sequence matrix	Mx P logical matrix; column `i` marks region samples for category `i`. Use `Categories` NV-pair to name the columns; otherwise they are labeled `"1" .. "P"`.	`signalMask([0 1 1 0; 0 0 1 1]', Categories=["A" "B"])`	`"binarySequences"`

If SampleRate is set, ROI-table limits are interpreted as seconds (not sample indices) and rounded to nearest positive integer.

Construction NV-pairs

Settable at construction (also writable after, except where noted):

NV-pair	Type	Default	Notes
`SampleRate`	positive numeric scalar	unset	When set, ROI-table limits are interpreted as seconds. When unset, limits are sample indices. Read-only after construction.
`Categories`	string vector / cell of char	inferred (`["1" .. "P"]` for binary source)	Settable only when `src` is a binary-sequence matrix; read-only otherwise.
`LeftExtension`	nonnegative integer	`0`	Extend each region this many samples to the left. Truncated at sequence start.
`RightExtension`	nonnegative integer	`0`	Extend each region this many samples to the right.
`LeftShortening`	nonnegative integer	`0`	Shorten each region this many samples from the left. Regions shortened to zero length are removed.
`RightShortening`	nonnegative integer	`0`	Shorten each region this many samples from the right.
`MergeDistance`	nonnegative integer	`0`	Merge same-category regions separated by this many samples or fewer. Contiguous / overlapping / repeated regions are always merged.
`MinLength`	positive integer	`1`	Drop regions shorter than this many samples.

Modification order applied by the object: extend → shorten → merge → drop-short.

Read-only / post-construction-only properties (cannot be set as NV-pairs at construction): SourceType, SpecifySelectedCategories, SelectedCategories.

`catmask` overlap NV-pairs

NV-pair	Type	Default	Notes
`OverlapAction`	`"error"` \| `"prioritizeByList"`	`"error"`	When `"error"`, `catmask` throws if regions of different categories overlap. When `"prioritizeByList"`, overlapping samples are resolved by priority.
`PriorityList`	integer vector indexing `Categories`	`Categories` order	Used when `OverlapAction="prioritizeByList"`. First entry is highest priority; must contain indices for all entries in `Categories`.

binmask and roimask accept no overlap-handling NV-pairs (they don't need one — binmask returns one column per category, and roimask returns the ROI table directly).

Object methods

Method	Returns
`catmask(msk[, len])`	Categorical sequence; one label per sample, `<undefined>` outside ROIs.
`binmask(msk[, len])`	Logical matrix, one column per category (size `len × P`). Not a single-class vector.
`roimask(msk)`	ROI table (round-trip).
`extractsigroi(msk, signal)`	Cell array of per-region slices of `signal`.
`plotsigroi(msk, signal)`	Plots `signal` with regions colored by category — sanity-check helper.

Canonical pattern

% First variable is an Mx2 matrix of limits; second is the label array.
% Column names are not required to be "ROILimits" / "Value".
rois = table([100 400; 500 900], categorical(["A"; "B"]));
msk  = signalMask(rois);

% Per-sample categorical for a 1000-sample signal.
% (len is required because SourceType is "roiTable".)
seq  = catmask(msk, 1000);
% seq is 1000x1 categorical with values <undefined>, A, or B.

If your data has overlapping ROIs across categories, supply OverlapAction explicitly — the default errors:

% Waveform-segmentation pattern: prefer QRS over P over T.
% PriorityList indexes into Categories; first index = highest priority.
seq = catmask(msk, signalLength, ...
    OverlapAction="prioritizeByList", ...
    PriorityList=[2 1 3]);

Per-sample vs per-frame

Need	Use
One label per sample (dense segmentation)	`signalMask` + `catmask`
One label per frame (windowed classification)	`framelbl` — see fn-framelbl.md

Don't materialize per-sample with catmask and then pool to per-frame by hand. framelbl consumes the ROI table directly and is faster.

Anti-pattern — hand-rolled fill

Don't manually index a table to fill a per-sample vector. catmask does this in one call.

% Bad — hand-rolled fill:
seq = repmat(categorical(missing), 1000, 1);
for i = 1:height(rois)
    seq(rois.Var1(i,1):rois.Var1(i,2)) = rois.Var2(i);
end

% Good:
msk = signalMask(rois);
seq = catmask(msk, 1000);

Gotchas

signalMask is lazy. The object holds the source; catmask / binmask / roimask materialize. For very long signals you may not want to materialize at all — query specific ranges via extractsigroi instead.
binmask is per-category, not single-class. Output is a len × P logical matrix (one column per category). Don't bind it to a binVec variable expecting a vector.
Method name is catmask, not getmask. Calling getmask(msk, N) on a signalMask object errors with "Unrecognized method". Note: the Waveform Segmentation Using Deep Learning doc example defines a user-helper file getmask.m that wraps catmask — that's a private helper in the example, not a method on the object.
OverlapAction defaults to "error". catmask(msk, len) with no NV-pair throws when regions of different categories overlap. Pass OverlapAction="prioritizeByList" (with optional PriorityList) to resolve overlaps. binmask doesn't need this — separate column per category.
Samples not covered by any ROI are <undefined>. If your segmentation expects a "background" class, fill explicitly: fillmissing(seq, "constant", "background").
SampleRate flips ROI-limit units. Without SampleRate, limits are sample indices; with SampleRate=Fs, they are seconds. Switching between the two re-interprets the same numbers.
Modification order is fixed: extend → shorten → merge → drop-short. Stacking LeftExtension=2 and LeftShortening=2 is not a no-op — extension happens first, then shortening trims from the new left edge.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

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Attribution and licence

Created by

MathWorks

Publisher

MathWorks (Official)

Source

matlab/matlab-agentic-toolkit · skills-catalog/signal-processing/matlab-prepare-signal-data · 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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2026-11-08

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