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Feature-wise CCC rates for top-performing handcrafted Feature Representations (FR).

Code coming soon!

VAM database

Dimension Type FR ID FR desription CCC
Valence Sysytem mfcc_sma[1]_upleveltime75 segment duration where signal is above 0.75*range of 1st MFCC coefficient 0.199
Valence System mfcc_sma[3]_percentile1.0 first percentile of 3rd MFCC coefficient 0.171
Arousal System audspec_lengthL1norm_sma_quartile3 3rd quantile for magnitude of L1 norm of Auditory Spectrum 0.725
Arousal System audspec_lengthL1norm_sma_peakMeanAbs arithmetic mean of peaks for L1 norm of Auditory Spectrum 0.716
Dominance System audspec_lengthL1norm_sma_percentile99.0 99th percentile for L1 norm of Auditory Spectrum 0.659
Dominance System audspec_lengthL1norm_sma_peakMeanAbs arithmetic mean of peaks for L1 norm of Auditory Spectrum 0.669

IEMOCAP database

Dimension Type FR ID FR desription CCC
Valence Sysytem audspec_lengthL1norm_sma_de_flatness contour flatness for delta values of magnitude of L1 norm of Auditory Spectrum 0.166
Valence Sysytem audSpec_Rfilt_sma_de[23]_flatness ontour flatness for delta coefficient of 23rd coefficient of Relative Spectral Transform (RASTA)-style filtered applied to Auditory Spectrum 0.144
Arousal Sysytem mfcc_sma[2]_range range of 2nd MFCC coefficient 0.469
Arousal Sysytem mfcc_sma[2]_pctlrange0-1 he range of the 1% and the 99% for 2nd MFCC coefficient 0.473
Dominance Sysytem mfcc_sma[2]_pctlrange0-1 he range of the 1% and the 99% for 2nd MFCC coefficient 0.375
Dominance Sysytem mfcc_sma_de[4]_lpc1 1st linear prediction filter coefficient for the delta coefficient of 4th MFCC coefficient 0.355

For both datasets, the top feature-wise CCC rates. FR represents vocal tract systems. Descriptions of the top-performing FR can be found in the four columns of tables.

The Python code for extracting WavLM FR used in our study can be found in the following repository: https://github.com/idiap/ExVo-2022

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