There are 21 baseline features that formed the initial research into diagnosing PD using vocal data. There are five features that measure the jitter: the instability of the fundamental frequency of the vocalization. Six features measure the shimmer: the instability of the amplitude of the vocalization. Five features characterize the fundamental frequency and its non-time-series statistical properties. The remaining features quantify the signal-to-noise ratio, signal entropy, self-similarity, and logarithmic entropy.

### Time-frequency features

There are 11 features that deal with the timbre of the voice in the time-frequency domain. Three such features measure the intensity of the vocalization. Four measure the strength of the first four formats of the timbre. An additional four measure the spacing between the formats.

### Mel Frequency Cepstral Coefficients

Eighty-four (84) features are used to measure non-vocal-fold effects. Vocalizations are complex physical processes and employ many muscles in their formation. These MFCC features attempt to characterize these effects that take place in other portions of the vocal tract than the folds.

### Wavelet Transform Features

These 182 features are derived from an orthogonal wavelet transform that has been normalized to the fundamental frequency. Unlike the tunable Q wavelets of Sakar et al., these wavelets are 