Skill v1.0.1
Automated scan85/100+1 new
version: "1.0.1" name: matlab-prepare-signal-data description: | Use this skill when building a signalDatastore pipeline for ML training: loading signals from .mat / .csv / .dat folders (and .wav when Audio Toolbox is unavailable), deriving labels (filename, folder, in-file column, ROI), splitting into train/val/test, framing long signals for per-frame supervision, parallel processing across a parpool, or shaping a datastore output for trainnet. Triggers include the function names signalDatastore, filenames2labels, folders2labels, splitlabels, countlabels, framesig, framelbl, signalMask, catmask, and workflow phrases like "labels from filenames", "stratified split", "ReadFcn for signalDatastore", "load mat/csv/wav for training". license: MathWorks BSD-3-Clause metadata: author: MathWorks version: "1.0"
Prepare Signal Data for ML Training
Look in Signal Processing Toolbox first. The labeling, splitting,framing, and partitioning helpers forsignalDatastorelive in SignalProcessing Toolbox — not in Stats & ML Toolbox or generic-MATLAB stringutilities.
When to Use
Loading or preparing signal / time-series data for ML training in MATLAB. Reach for this skill especially when you want:
- labels derived from filenames or folder names
- stratified train/val/test splits over a datastore
- per-frame labels from ROI tables on long signals
- parallel processing of signals across a parpool
- a datastore shaped to feed
trainnet
When NOT to Use
- Raw `.wav` audio classification with Audio Toolbox available.
audioDatastore is the canonical path. If Audio Toolbox is not available, this skill's custom-ReadFcn workflow handles .wav via base-MATLAB audioread — see references/wf-custom-readfcn.md.
Best practices
- Deliverable is a runnable `.m` script the user can save and re-run —
not workspace state. The output of this skill is a reusable data-prep pipeline the user can version and hand off.
§ 0 Common reflexes
If your first instinct is one of these, the canonical replacement is one row away.
| Reflex | Canonical | Detail | |
|---|---|---|---|
Custom ReadFcn for a .csv | signalDatastore default reader + SignalVariableNames | references/fn-signaldatastore.md | |
cvpartition for a datastore split | splitlabels + subset(ds, idx{k}) (cell-array indexing) | references/fn-splitlabels.md | |
regexp / extractBefore / fileparts to derive labels from filenames | filenames2labels(sds, Extract=...) | references/fn-filenames2labels.md | |
regexp / hand-rolled fileparts(fileparts(...)) for labels from subfolders | folders2labels(sds.Files) — pass the datastore's file list | references/fn-folders2labels.md | |
parfor i = 1:numel(ds.Files) constructing a fresh datastore per file | partition(ds, N, k) per worker | references/fn-partition.md, references/wf-parallel-process.md | |
Manual framing loop with (i-1)*hopSize+1 | framesig(x, fl, OverlapLength=...) | references/fn-framesig.md, references/wf-frame-and-label.md | |
Manual ROI-to-frame label vote with containers.Map | framelbl(rois, ConsolidationMethod=..., PriorityList=...) | references/fn-framelbl.md, references/wf-frame-and-label.md | |
for loop calling load(file) to extract labels from in-file variables | signalDatastore(folder, SignalVariableNames=["x","label"]) — the loop goes away; both variables come back per read(sds) as a cell row | references/fn-signaldatastore.md |
§ 1 Workflows
Each workflow file is the entry point; it links the function-detail files you'll need at each step.
| Workflow | Use when | Reference (entry → chain) | |
|---|---|---|---|
| Load + label + split | Building a datastore from a folder of files for training. | wf-load-and-split.md → fn-signaldatastore, fn-filenames2labels / fn-folders2labels, fn-countlabels, fn-splitlabels, fn-subset | |
| Frame long signals + per-frame labels | Signals are long; supervision is per-window. | wf-frame-and-label.md → fn-framesig, fn-framelbl, fn-signalmask-getmask | |
| Parallel processing across a parpool | Computing per-signal results across workers. | wf-parallel-process.md → fn-partition | |
| Custom ReadFcn (only when needed) | File format isn't .mat / .csv, or has a metadata prelude. | wf-custom-readfcn.md → fn-signaldatastore | |
| Hand-off to `trainnet` | Datastore is ready; next step is shaping for trainnet / combine / arrayDatastore (routes to minibatchqueue / dlarray for custom batching or GPU prefetching). | wf-handoff-to-dl.md |
§ 2 Functions
| Function | Used for | Reference | |
|---|---|---|---|
signalDatastore | Datastore constructor (.mat / .csv / custom). | references/fn-signaldatastore.md | |
filenames2labels | Categorical labels from filename pattern. | references/fn-filenames2labels.md | |
folders2labels | Categorical labels from containing-folder name. | references/fn-folders2labels.md | |
splitlabels | Stratified train/val/test index sets. | references/fn-splitlabels.md | |
countlabels | Per-class file count for balance checks. | references/fn-countlabels.md | |
subset | Slice a datastore by index (single-process). | references/fn-subset.md | |
partition | Slice a datastore across parpool workers. | references/fn-partition.md | |
framesig | Frame a signal into windows with overlap. | references/fn-framesig.md | |
framelbl | Collapse ROI rows into per-frame labels. | references/fn-framelbl.md | |
signalMask / catmask / binmask | Per-sample masks from ROI tables. | references/fn-signalmask-getmask.md |
§ 3 Highest-frequency canonical patterns (inline)
3.1 CSV is first-class — no custom ReadFcn for tabular CSV
sds = signalDatastore(folder, ...FileExtensions=".csv", ...SignalVariableNames=["ch1","ch2"]);
Don't wrap readtable(..., 'SelectedVariableNames', ...) in a custom ReadFcn. The default reader does this directly. Full table: references/fn-signaldatastore.md.
3.2 Filename labels — position-independent extraction
labels = filenames2labels(sds, Extract = "G" + digitsPattern);
Don't reach for extractBefore("_") / regexp — silent wrong labels when filename format varies. More patterns: references/fn-filenames2labels.md.
3.3 Stratified split — splitlabels + subset
splitIndices = splitlabels(labels, [0.7 0.15 0.15]);sdsTrain = subset(sds, splitIndices{1});sdsVal = subset(sds, splitIndices{2});sdsTest = subset(sds, splitIndices{3});% splitIndices{4} exists but is empty here (ratios sum to 1).
splitlabels returns an (N+1)-element cell array of index vectors. When sum(ratios) == 1 (as above) the last cell is empty; when sum(ratios) < 1 the last cell holds the leftover indices.
Don't use cvpartition for datastore-backed splits — requires materializing labels first and doesn't compose with subset. Full contract: references/fn-splitlabels.md.
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