matlab 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:
labeledSignalSetfor Signal Labeler, all label types.
When NOT to Use
- Raw
.wavaudio classification with Audio Toolbox available.audioDatastoreis the canonical path (this skill's custom-ReadFcnworkflow handles.wavonly 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-filterskill. 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-featuresskill. This skill'sframesig/framelblare for manual per-window labeling / supervision, not for deriving a feature table; thesignal*FeatureExtractorobjects window internally and emit the table.
Best practices
- Deliverable is a runnable
.mscript the user can save, version, and re-run — not workspace state. - Prefer the highest-level function that does the job.
detrend/smoothdata/fillmissing/resampleread 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 lengthflas an argument — it does NOT hide it — so prefer it over callingsgolayfiltdirectly. Reach forsgolayfiltonly for what the dispatcher genuinely lacks (derivative output viadn, or an unusual polynomial order).
- The high-level call usually exposes the control you think you need. In
particular
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
NaNbefore any step that can't handle missing data (detrend, filters, most smoothers). - Align / resample: putting a signal on a new grid (
retime/synchronize) createsNaNat 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.
Copyright 2026 The MathWorks, Inc.