# Objective Assessment of Porcine Voice Acoustics for Laryngeal Surgical Modeling
## 2 Materials and Methods
### Parameter Analysis  
For parameter calculation, recordings were converted to mono by discarding the right audio channel and scaled to \(-1\), 1 range as no absolute volume could be determined in our recording environment. In total, 14 different parameters and versions of parameters that were previously used on aperiodic pig voice were chosen. Additionally, we included three classic F0-based voice measures. We mainly implemented parameters that were previously described to differentiate squaels from other pig phonation, as squaels generally seemed to become more \"grunt-like\" (but still distinguishable from grunts) after surgery [15; 16; 17].  
In total, 13 parameters were implemented in Matlab (version R2020b). Harmonics-To-Noise ratio was calculated using Praat (version 6.1.38), as described in [15]. Jit, Shim, and CPP were also calculated using Praat with default settings with exception of the \"voicing threshold\"-setting, which was set to 0 to allow for F0 detection in the entire squeal (otherwise calculation of these parameters would not have been possible for some squaels). While great care has been taken to implement the parameters exactly as described in their respective sources, exact reproduction cannot be guaranteed, in part due to incomplete descriptions in the source material. To enhance the reproducibility of our work, the Praat script and Matlab code used to calculate these parameters in this work are provided in the supporting information (Files S1-S3). Names, abbreviations, sources, units, and a brief description of all parameters are listed in Table 2. Table S1 tabulates the full dataset of parameter values for all squaels and pigs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter and Reference** & **Abbreviation** & **Unit** & **Windowing** & **Description** \\ \hline Peak frequency [16] & PF & Hz & total &
\begin{tabular}{c} Position of the highest peak in \\ the energy spectrum of the entire \\ signal. \\ \end{tabular} \\
50\% energy spectrum quantile [16] & Q50 & Hz & total &
\begin{tabular}{c} Frequency that divides the \\ energy spectrum of the entire \\ signal in two intervals of equal \\ energy. \\ \end{tabular} \\
50\% first window quantile [16] & Q50\({}_{2}\) & Hz & partial &
\begin{tabular}{c} Eleven evenly spaced hamming \\ windows are calculated within \\ the signal. Q50\({}_{2}\) is the Q50 of the \\ second window. \\ \end{tabular} \\
50\% last window quantile [16] & Q50\({}_{10}\) & Hz & partial & \
\begin{tabular}{c} Eleven evenly spaced hamming \\ windows are calculated within \\ second window. \\ \end{tabular} \\ Minimum 50\% window quantile [16] & Q50\({}_{\text{min}}\) & Hz & partial &
\begin{tabular}{c} Eleven evenly spaced hamming \\ windows are calculated within \\ the signal. Q50\({}_{\text{min}}\) is the lowest \\ Q50 of the nine inner windows. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Name, reference, units, general type of windowing, and a brief description of each investigated parameter.  
Some of the parameters are similar and differ mainly in the windowing of the signal or the signal sections on which they are calculated. In Figure 2, we illustrate the varying types of time windowing (total, partial, and consecutive) for one example squeal. \"Total\" refers to a parameter calculated on one single window including the entire squeal, \"partial\" refers to a parameter only calculated for a small subsection of the signal in time, and \"consecutive\" refers to a parameter calculated for multiple consecutive, overlapping windows that is then averaged to obtain the final parameter value for the squeal.