Summary Table of Signal Features
The table below summarizes all the features covered in this article. It provides a quick reference for their type, dependency on the FFT, and primary applications.
| Feature | Type | Depends on FFT? | Primary Application |
|---|---|---|---|
| Spectral Centroid | Spectral | Yes | Brightness, timbre |
| Spectral Spread | Spectral | Yes | Tonality vs. noise |
| Spectral Skewness | Spectral | Yes | Spectral asymmetry |
| Spectral Kurtosis | Spectral | Yes | Impulsiveness, peaks |
| Spectral Roll-off | Spectral | Yes | Instrument distinction |
| Spectral Flux | Spectral | Yes (2+ frames) | Onset detection |
| Spectral Entropy | Spectral | Yes | Tonality vs. noise |
| Spectral Flatness | Spectral | Yes | Noise vs. tonal detection |
| MFCC | Cepstral | Yes | Speech recognition |
| LPCC | Cepstral | Yes (via LPC) | Speech coding |
| Chroma | Chromatic | Yes | Chords, harmony |
| Modulation Spectra | Modulation | Yes (2D) | Rhythm, tempo, emotion |
| Zero-Crossing Rate (ZCR) | Time-domain | No | Voiced/unvoiced |
| RMS Energy | Time-domain | No | Dynamics, loudness |
| Crest Factor | Time-domain | No | Impulsiveness |
This table is intended as a quick reference. For detailed derivations, code examples, and practical advice, refer to the individual articles on each feature group.
The choice of which features to use depends entirely on the application. In speech recognition, MFCCs dominate. In music analysis, chroma and modulation spectra are indispensable. In vibration monitoring, spectral kurtosis and crest factor are the first things to check. And in many cases, a combination of features from different domains gives the best results.
Keep in mind that these features are not meant to be used blindly. Understanding what each feature measures, how it behaves, and what its limitations are is essential for applying them effectively. The mathematics is straightforward — the art lies in choosing the right features for the right problem.