Hello, researchers, students, and other signal hobbyists! FeatureFinder is a free MATLAB tool that lets you organize your signals, visually review them with speed and ease, apply filters and normalization, and export meaningful signal features in a ready-to-analyze format.
FEATURE PREVIEW The feature value for the selected file is instantly updated as settings are changed.
FFT VIEWER Quickly and easily view the frequency spectrum of any data file.
PROFILES Store the filenames and settings for each of your experiments.
FILE FINDER Unique file-finding algorithm finds all of your data files in seconds.
CUSTOMIZE Add your own filters in just a few lines of MATLAB code.
CUSTOMIZE Add your own features in just a few lines of MATLAB code.
PROCESS Processing your data differently is as easy as revising the settings and clicking "Process."
EASY BROWSING Scroll through files one-by-one, or hone in using the drop-down menus.
EXPORT DATA Not interested in signal features? Use FeatureFinder solely as a filtering tool with the export buttons.
What makes FeatureFinder really stand out is the option for you to code your own features and filters, allowing unlimited flexibility—you can literally extract any feature that you like from your data. Did we mention that it's free? Check out The Story to find out more!
We’re very excited to announce a brand new version of FeatureFinder: Version 2.5.0. It has the usual mix of big additions, minor revisions, and the odd bug fix, so we’d definitely recommend giving it a try. Download a copy, free as always, here. Here are a couple things to expect:
Frequency mode
Viewing the frequency spectra of our signals is something we’ve been keen to integrate for a while. You can now simply check an “FFT” checkbox to see the spectrum of any loaded signal. (FFT stands for “Fast Fourier Transform.”) This can be useful in many ways, including these:
Search your data’s spectra for frequency-specific noise, and see how well your filters reduce it;
Look for patterns among your data files’ spectra, and build custom features to analyze them;
Develop a better intuitive sense about the frequency content of your data;
Learn how different filters work, and the effects of their cut-off frequencies and filter orders on your data.
