general-purpose and form an orthogonal basis for representing the vocalization instead of an overcomplete one. This makes them easy to use, but potentially insufficient for inference.

### Vocal Fold Features

These 22 vocal fold features provide a more detailed quantification of the baseline vocal fold features described above. The Glottis quotient, with 3 features, focuses on the periodicity of glottal closure. The Glottal-to-Noise Excitation, with 6 features, quantifies the signal-to-noise ratio more deeply. The Vocal Fold Excitation Ratio, with 7 features, also quantifies the signal-to-noise ratio but uses non-linear methods to do so. Lastly, the Empirical Mode Decomposition, with 6 features, uses elementary basis functions and measurements of entropy to decompose the signal similarly to a wavelet transform.

### Tunable-Q Wavelet Transform

The remaining 434 features are derived using the tunable-Q wavelet transform suggested and implemented by Sakar et al. This transform is overcomplete and will consequently have redundancy in its results. However, this overcompleteness allows for more intuitive features after a selection process is used. Since the basis functions are uniformly spaced with respect to human experience, they also align well with respect to human vocalization. These features tend to be selected not only due to their number, but 