Matlab prepare signal data

Prepare Signal Data for ML Training

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

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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