Topics Map > Resources for collaborators
Validating pilot data
Making sure your experiment is ready for prime time.
After piloting your experiment, here's a few tools you can use to validate your data.
[onset_bad, offset_bad] = validate_onsetOffset(dataPath, badThresh, bInterpret)
- WHAT IT DOES: This function shows histograms of how close a speaker's voice onset and offset were to the edge of the recording. Use to validate that you were recording the whole utterance.
- WORKS FOR: Audapter experiments.
- Optionally returns two output arguments, which are vectors of trial numbers too close to the onset/offset respectively.
validate_formantShift(dataPath, plotParams, bInterpret)
- WHAT IT DOES: Plots the average formants from signalIn and compares it against the average formants from signalOut on an [F1 F2] field. Shows different plots for different conditions in expt.conds. Use to visually validate that the proper F1/F2 perturbations were applied in different conditions.
- WORKS FOR: Formant perturbation experiments, especially ones where the same perturbation is applied over the whole utterance.
[Tables] = validate_exptSetup(dataPath, groupings, bInterpret)
- WHAT IT DOES: Shows the number of trials in pairs of groupings; for example, how many trials were both expt.word{1} and expt.cond{1}, expt.word{2} and expt.cond{1}, etc. Useful for making sure your counterbalancing across different manipulations is happening properly.
- WORKS FOR: Any experiments with counterbalanced groupings. E.g., words + conds (various), words + conds + colors (stroop), words + conds + dots (attentionComp).
[figHandle] = plot_audapterFormants(data, plotParams, bInterpret)
- WHAT IT DOES: Simply displays the wave form (top), spectrogram (bottom), signalIn formant track (bottom; cyan), and signalOut formant track (bottom; magenta).
- WORKS FOR: Any Audapter experiment.
- The first input parameter takes a data.mat file. However, this function is intended to run on 1-10 trials at a time. You should view only a subset of the data file at a time, such as plot_audapterFormants(data(1:10), [], []).
[] = check_trialDurations(dataPath, bPlot)
- WHAT IT DOES: Plots the duration of audio recorded for all trials in an experiment. Helps validate that trial duration was consistent across all trials.
- WORKS FOR: Any experiment that records Audapter audio
- It's expected that there are 10-20 ms variations in trial length across some trials.