matlab-prepare-signal-data

name	matlab-prepare-signal-data
description	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`.
license	https://www.mathworks.com/content/dam/mathworks/license/pmrl/license.md
metadata	{"author":"MathWorks","version":"1.1"}

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

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.

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