# Objective Assessment of Porcine Voice Acoustics for Laryngeal Surgical Modeling
## 1 Introduction
In the context of vocal fold surgery on pigs, two notable studies need to be mentioned: [PERSON] et al., implanted hemi-larynx replacements into six White/Landrace cross-bred pigs [7]. Squeal and grunt voice samples were collected but spectrograms remained altered six months after surgery. [PERSON] et al., implanted a different hemi-larynx replacement in three Yucatan mini-pigs [19]. They reported phonation amplitude of isolated squeals measured at a fixed microphone distance and found recovery of baseline amplitude after eight weeks. However, other voice quality measures were not assessed. These studies illustrate the need for further research in quantitative porcine acoustic analysis relevant to laryngeal surgery. Various software tools exist to examine human phonation and voice, but tools designed for human voices do not necessarily work on pigs. Common acoustic parameters including Jitter, Shimmer, and many noise measures are intended for analysis of sustained phonation with distinct fundamental frequency (F0) [20; 21]. Porcine phonation, in contrast, is often aperiodic, so these parameters are not expected to generate reliable or meaningful results [15; 16; 17]. Furthermore, although voice parameters provide the possibility of objective voice assessment, many of them fail to reliably correlate with perceptual voice quality judgments [22]. Jitter and Shimmer in particular have been questioned due to the fact that they are poorly distinguished by auditory perception [23]. Objective parameters still serve a purpose in providing objectivity and uncovering subtle properties of acoustic signals, but parameters suitable for aperiodic voices are needed to evaluate pigs. Some parameters have been previously described for aperiodic pig voices and were used to differentiate between pig phonation types [15; 16; 17]. [PERSON] et al., in 2013 analyzed the piglet vocalization repertoire of 1513 calls from 84 (Large White \(\times\) Landrace) \(\times\) (Duroc \(\times\) Pietrain) piglets in different behavioral contexts. They identified a set of eight parameters using a cluster analysis approach that differentiated best between two or five types of calls [16]. [PERSON] et al., analyzed calls from 19 wild boars. Out of 19 acoustic parameters calculated using PRAAT and the MIR Toolbox in MATLAB, four parameters were selected to best differentiate between four call types [17]. In contrast, [PERSON] et al. investigated parameter change with emotional state in 88 Large White \(\times\) Landrace piglets. They used four parameters calculated with Avisoft SASLab and PRAAT and found distinct correlations with emotion in squeals and grunts [15]. Further, all of these authors mention the problem of not clearly determinable pig phonation F0 from acoustics and only used parameters suited for aperiodic voice [15; 16; 17]. All of these parameters were previously only used to differentiate between call types of healthy pigs (or boars). To the best of our knowledge, no parameters have been introduced that were explicitly designed or tested to differentiate between pig voice before and after laryngeal injury or surgery. We have observed by acoustic perception that early after vocal fold surgery, pig vocalizations become more \"grunt-like\". We therefore hypothesize that the parameters identified in these three previous works may also be able to differentiate between pre- and post-surgery squeals. For this reason, we implemented seven parameters that were used by [PERSON] et al. [16], three parameters by [PERSON] et al. [17], and three additional parameters used by [PERSON] et al. [15] as well as the length of the call, which was included in all three studies [15; 16; 17]. For demonstrative purposes, we also calculated three classical F0-based human voice measures (Jitter (%) (Jit), Shimmer (%) (Shim), and Cepstral peak prominence (CPP)). Since no exact calculation algorithms were given in these previous works, it cannot be guaranteed that the parameters are perfectly reproduced.