AC	Autocorrelation		LPCC	Linear predictive coding coefficients
AF	Average frequency		LMS	Log-energy
BC	Binary classifier		MF	Mean frequency
BF	Butterworth filter		MFCC	Mel-frequency cepstral coefficients
BT	Bagged tree		MPT	Maximal phonation time
CF	Crest factor		MxF	Maximum frequency
CV	Chroma vector		NHR	Noise-to-harmonic ratio
CAD	Cough automatic detection		NLE	Non-linear entropies
CNN	Convolutional neural network		NLF	Non-linear features
CQT	Constant-Q transform		NMFC	Non-negative matrix factorization coefficients
CPP	Cepstral peak coefficients		NVB	Number of voice breaks
DCN	Dense convolutional network		O	Onset
DT	Decision tree		PCR	Polymerase chain reaction
DVB	Degree of voice breaks		PSD	Power spectrum density
E	Energy of the signal		RASTA-PLP	Relative spectra perceptual linear prediction
EE	Entropy of the energy		RF	Random forest
ET	Extremely randomized trees		RMS	Root mean square
EVR	Eigenvalue ratios		RNN	Recurrent neural network
FBC	Filter bank coefficients		SB	Spectral bandwidth
FNN	Feedforward neural network		SC	Spectral centroid
F ${}_0$	Fundamental frequency		SCn	Spectral contrast
F ${}_n$	Formant frequencies		SE	Spectral entropy
GBF	Gradient boosting framework		SI	Spectral information
GTCC	Gamma-tone cepstral coefficients		SF	Spectral flux
HD	Hjorth descriptors		Sh	Shimmer
HFD	Higuchi fractal dimension		Sk	Skewness
HNR	Harmonic-to-noise ratio		SP	Spectral flatness
HR	Harmonic ratio		SR	Spectral roll-off
J	Jitter		s-RQA	Symbolic recurrence quantification analysis
K	Kurtosis		SVM	Support vector machine
KFD	Katz fractal dimension		TC	Tonal centroid
k-NN	k-Nearest neighbors		TECC	Teager energy cepstral coefficients
LE	Log-energy		TFM	Time-frequency moment
LMS	Log-Mel spectrogram		ZCR	Zero crossing rate