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://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/wf-load-and-split.md

Workflow: load + label + split

Functions used: signalDatastore, filenames2labels, folders2labels, splitlabels, subset, countlabels

A folder of files becomes three datastores (train/val/test). Four steps: construct → label → check distribution → split.

Off-ramp. If supervision is per-frame inside long signals (one file contains many labeled windows), see wf-frame-and-label.md instead.

Recipe

Step 1 — Construct the datastore

Pick the constructor based on file format and where the signal lives:

Input shape	Constructor	Detail
`.mat` files, single signal variable	`signalDatastore(folder, FileExtensions=".mat")`	fn-signaldatastore.md
`.mat` files, named variable(s)	`signalDatastore(folder, FileExtensions=".mat", SignalVariableNames="x")`	fn-signaldatastore.md
`.csv` files, single column	`signalDatastore(folder, FileExtensions=".csv")`	fn-signaldatastore.md
`.csv` files, multi-column (pick subset)	`signalDatastore(folder, FileExtensions=".csv", SignalVariableNames=["ch1","ch2"])`	fn-signaldatastore.md
`.csv` files, sample-rate column	`signalDatastore(folder, FileExtensions=".csv", SampleRateVariableName="fs")`	fn-signaldatastore.md
Non-tabular format (custom binary, metadata prelude)	Custom `ReadFcn`	wf-custom-readfcn.md

Always set FileExtensions when the data folder also contains scripts or sub-folders. The NV-pair is the canonical filter — not a glob in the location.

Anti-pattern. Don't reach for a custom ReadFcn for tabular CSV. The default reader covers it via SignalVariableNames.

Step 2 — Derive labels

Pick the label source:

Where labels live	Call	Detail
In the filename (e.g. `subj_G42.mat`)	`filenames2labels(sds, Extract = "G" + digitsPattern)`	fn-filenames2labels.md
In the containing subfolder name (e.g. `data/classA/file01.mat`)	`folders2labels(location)`	fn-folders2labels.md
In a column / variable inside the file	`SignalVariableNames` on the constructor — load the label alongside the signal	fn-signaldatastore.md
In an ROI table inside the file	`signalMask` + `catmask` for per-sample, `framelbl` for per-frame	fn-signalmask-getmask.md, wf-frame-and-label.md

Anti-pattern. Don't write regexp / extractBefore("_") / extractBetween("_","_") for filename labels. Position-based extraction silently produces wrong labels when filename format varies. See fn-filenames2labels.md for position-independent forms.

Anti-pattern. Don't write a for loop calling load(file) to extract labels from in-file variables. That eager-loads gigabytes to read kilobytes. Use SignalVariableNames to name the label column directly.

Step 3 — Check the distribution

countlabels(labels)

One-line balance check. If a class has too few samples for the split ratios you want, splitlabels will tell you in step 4.

Step 4 — Stratified split

splitIndices = splitlabels(labels, [0.7 0.15 0.15]);
sdsTrain = subset(sds, splitIndices{1});
sdsVal   = subset(sds, splitIndices{2});
sdsTest  = subset(sds, splitIndices{3});

splitlabels returns an (N+1)-element cell array of index vectors, where N is the number of split ratios. Full contract in fn-splitlabels.md.

Anti-pattern. Don't use cvpartition for datastore-backed splits. It requires materializing labels first and doesn't compose with subset(ds, idx). The canonical chain is splitlabels → subset.

To verify the split is stratified, re-run countlabels per split:

countlabels(labels(splitIndices{1}))   % train
countlabels(labels(splitIndices{2}))   % val
countlabels(labels(splitIndices{3}))   % test

The per-class proportions should match across splits (splitlabels stratifies by default).

The three datastores are ready for training; trainnet-side shaping is in wf-handoff-to-dl.md.

Worked end-to-end example

sds = signalDatastore("dataset", FileExtensions=".mat", SignalVariableNames="x");
labels = filenames2labels(sds, Extract = "G" + digitsPattern);
disp(countlabels(labels))

splitIndices = splitlabels(labels, [0.7 0.15 0.15]);
sdsTrain = subset(sds, splitIndices{1});
sdsVal   = subset(sds, splitIndices{2});
sdsTest  = subset(sds, splitIndices{3});

Next in the chain

Per-frame supervision inside long signals (after this workflow, or instead of it for a per-window task) → wf-frame-and-label.md.
Hand-off to trainnet / dlarray (the immediate next step once the three datastores are ready) → wf-handoff-to-dl.md.

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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2026-11-09

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