I compared most of the voice characteristics anywhere between men and women grunts so you can sample having gender-certain variations

Grunts and you will strong grunts one another consist of repeated issue. Since these repetitive elements differed most into two grunt brands, we called her or him in a different way: ‘pulses’ getting grunts, and ‘sound cycles’ to have deep grunts. We utilized the system PRAAT 5.4.01 () towards voice analyses.

We chosen high-top quality grunts and you will strong grunts by the merely including those who work in new research away from sound properties, which had a code-to-sounds ratio from dos or maybe more for the three pulses/voice time periods on the highest amplitude. To take action, we opposed brand new voice tension of one’s pulse/cycle toward third large amplitude into voice pressure off about three randomly picked items regarding record noises within this 0.5 s before the grunt or strong grunt. If the voice stress of this heartbeat/duration was at the very least doubly large given that record sounds, we analysed the newest functions of one’s grunt or strong grunt. Towards investigation of characteristics of the grunt designs, we felt five details: 1. quantity of pulses/time periods for each voice, dos. lifetime of the brand new voice, step three. amount of pulses/cycles for each next, cuatro. dominating frequency.

So you can measure the number of pulses/cycles per voice, we noted most of the discernible heartbeat/course throughout the wave form of each and every grunt on no crossing adopting the highest top on heart circulation/cycle and you can counted brand new designated zero crossings. To choose the lifetime of a sound, i counted the amount of time amongst the designated no crossings of one’s very first and you may last discernible pulse/cylcle. So you’re able to calculate the number of pulses/cycles for every next, i split up exactly how many pulses/time periods of the duration of the fresh new voice. To choose the dominating regularity, we investigated the 3 loudest pulses inside a sound with the volume to your large sound strain and got the average of them around three frequencies.

With the data out-of sound functions getting ticks and you can plops, i just used music where we are able to demonstrably identify brand new sound-creating fish. I explained presses and you may plops having fun with a couple variables: 1. Prominent frequency, dos. voice strain difference in lower and higher frequencies.

To determine the principal regularity of your voice, i investigated the advantage spectrum of the brand new mouse click or plop getting the regularity on large sound pressure. We derived the advantage spectrum regarding the zero crossing of your waveform within high and you may reasonable amplitude. To calculate the voice strain distinction, we substracted new voice pressure level of the fifth harmonic out of the brand new sound pressure of the prominent frequency.

Review away from voice functions

Into the reviews off voice attributes, we first averaged the data for male sounds to the personal top. We were unable to do this for ladies, as there is actually no chance away from several times distinguishing personal girls during the the latest movies reliably.

Having ticks and you can plops, we very first examined to have gender-particular differences of one’s analysed functions

I opposed the fresh dominant volume and you may stage ranging from men grunts and you will deep grunts to choose differences when considering the two name types. I next checked to possess differences when considering the newest one another kind of solitary-pulse songs.

For statistical analyses, we first investigated the properties of the tested sounds for normality using Shapiro-Wilk tests. If data were normally distributed according to Shapiro–Wilk test (P > 0.05), we used t-tests to examine the differences in sound properties. If the Shapiro–Wilk test showed a significant deviation from a normal distribution (P < 0.05), we log-transformed the data to achieve normality, or used Mann–Whitney U tests where a normal distribution could not be achieved by data transformation. For the statistical analysis of sounds we used R (Version 3.3.1, We assumed a difference between sound properties to be significant if the P-value of the respective test was < 0.05.

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