# Premature baby cries remain different from those of full-term babies

Guillem Bonafos, Flavie Vial, Clément Cornec, Léo Papet, Lény Lego, Jessica Audouard, Aurélie de Vito, David Reby, Jérémy Rouch, Rémi Emonet, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Guillem Bonafos, Flavie Vial, Clément Cornec, Léo Papet, Lény Lego, et al. Premature baby cries remain different from those of full-term babies. Journal of the Acoustical Society of America, 2025, 157 (6), pp.4175 - 4183. 10.1121/10.0036842. hal-05245427

HAL Id: hal-05245427

https://hal.science/hal-05245427v1

Submitted on 8 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
# Premature baby cries remain different from those of full-term babies

Guillem Bonafos,<fnref n="1" />,<fnref n="2" />,ᵃ) Flavie Vial,<fnref n="3" /> Clément Cornec,<fnref n="1" /> Léo Papet,<fnref n="1" /> Lény Lego,<fnref n="1" /> Jessica Audouard,<fnref n="3" />

Aurélie De Vito,<fnref n="3" /> David Reby,<fnref n="1" />,<fnref n="4" /> Jérémy Rouch,<fnref n="1" /> Rémi Emonet,<fnref n="2" />,<fnref n="4" /> Hugues Patural,<fnref n="3" /> and Nicolas Mathevon<fnref n="1" />,<fnref n="4" />,<fnref n="5" />

## ABSTRACT:

Prematurity is known to increase the risk of organic or functional pathologies from the neonatal period as well as after birth. The impact of prematurity on the acoustic structure of cries, which are vital signals for survival of babies, has received little scrutiny, despite its prognostic potential. Here, we test the hypothesis that the acoustic structure of the cries of preterm babies changes with their growth, ultimately becoming identical to the cries of full-term infants. We recorded and analyzed the cries of 18 premature babies, born between gestational age 26 and 33 weeks, every week while they were hospitalized in a neonatal intensive care unit. Our results show that as premature babies approach their theoretical term, their cries become increasingly tonal, less marked by noisy components, and carrying individual cues. We then compared these cries with those recorded in 25 healthy full-term babies. Despite the evolution of premature baby cries over time, the acoustic structure of these cries remains different from that of full-term infants. These results suggest that the vocal maturity of premature babies is not fully attained at the theoretical age of term. © 2025 Acoustical Society of America. https://doi.org/10.1121/10.0036842

(Received 6 February 2025; revised 12 May 2025; accepted 15 May 2025; published online 4 June 2025)

[Editor: Zhaoyan Zhang]

Pages: 4175–4183

## I. INTRODUCTION

A vital signal for the survival of babies, the cry carries both static information signalling the individual identity of the babies (Lockhart-Bouron et al., 2023) and dynamic information depending on the physiological and emotional state of the babies (Schönweiler et al., 1996; Gustafsson et al., 2013; Bouchet et al., 2020; Corvin et al., 2024; Zeifman, 2001; Reby et al., 2016; Koutseff et al., 2018). The anatomical characteristics of the respiratory and phono- tory systems, as well as the associated neurophysiological processes, change with growth, accompanied by modifica- tions in the acoustic characteristics of the cries (Hartnick et al., 2005; Barbier et al., 2015; Laitman et al., 1978; Laitman and Reidenberg, 1993; Gilbert and Robb, 1996; Baeck and de Souza, 2007; Wermke et al., 2021). In healthy full-term infants, the pitch [fundamental frequency ($F_0$)] of cries varies considerably over the first 6 months of life (Gilbert and Robb, 1996), as does melodic complexity, mea- sured by variations in $F_0$ contours (Wermke et al., 2021). However, the cries of babies with specific pathologies follow different trajectories and display characteristics that distin- guish them from those of healthy babies (Barr et al., 2000;

Kheddache and Tadj, 2019). Cries are therefore biomarkers that can provide information about a child's health and pre- dict possible developmental problems (Ji et al., 2021; Hammoud et al., 2024). Specifically, the cries of children with autism spectrum disorder (ASD) (Esposito and Venuti, 2009; Sheinkopf et al., 2012; Orlandi et al., 2012; Esposito et al., 2017; Khozaei et al., 2020), brain damage (Onu et al., 2023), deafness (Hariharan et al., 2012), neurological dys- function (Lawford et al., 2022), laryngomalacia, cleft lip and palate, hearing impairment, and asphyxia or brain damage (Fuhr et al., 2015), show marked differences from the cries of healthy children.

Prematurity, i.e., being born before 37 weeks gesta- tional age, represents a high-risk situation for neurodevelop- mental disorders. Every year, more than one baby in ten is born prematurely. A significant proportion of them die from early or late complications. It is particularly important to monitor the development of premature babies and to detect any disorders at an early stage in order to increase their sur- vival. To improve the care of premature babies, simple, inexpensive diagnostic methods are thus needed to monitor their development. Automatic analysis of cries could be a valuable tool (Cabon et al., 2021). Previous studies have shown that preterm babies tend to have shorter, higher pitched cries, with a large amount of nonlinear acoustic

<footnote n="1">¹ENES Bioacoustics Research Lab, CRNL, University of Saint-Etienne, CNRS, Inserm, Saint-Etienne, France</footnote>

<footnote n="2">²Institut d'Optique Graduate School, Université Jean Monnet Saint-Etienne, CNRS, Laboratoire Hubert Curien UMR 5516, Inria F-42023, Saint-Etienne, France</footnote>

<footnote n="3">³Neonatal and Pediatric Intensive Care Unit, SAINBIOSE Laboratory, University of Saint-Etienne, Inserm F-42023, Saint-Etienne, France</footnote>

<footnote n="4">⁴Institut Universitaire de France, Paris, France</footnote>

<footnote n="5">⁵Ecole Pratique des Hautes Etudes, CHArt Lab, PSL University, Paris, France</footnote>

<footnote>ᵃ)Email: guillem.bonafos@univ-st-etienne.fr</footnote>
phenomena, such as chaos and biphonation (Michelsson
et al., 1983). It has been suggested that these traits change
with development and that the cries of premature babies
gradually becomes identical to those of full-term infants
(Michelsson et al., 1983). However, recent results suggest a
persistent difference in the acoustic structure of preterm
baby cries, even after reaching the theoretical age of term
(Orlandi et al., 2016). This difference would persist even
beyond the age of 1.5 years, with premature babies retaining
a higher pitch (Rautava et al., 2007) and a greater melodic
complexity (Shinya et al., 2017). However, in the absence
of longitudinal recordings during infant development, the
evolution of the acoustic characteristics of the cries of pre-
term babies from birth to theoretical term remains largely
unexplored. Specifically, no study has tested whether the
cries of premature babies have an individual acoustic signa-
ture. Yet, the individual signature is a characteristic feature
of the cries of full-term babies and plays a potentially
important role in interactions and attachment between care-
givers and the babies (Gustafsson et al., 2013).

In the present study, we test the hypothesis that the cries
of preterm babies become acoustically similar to those of
full-term babies when the babies reach their theoretical
term. We specifically examine whether the cries of prema-
ture babies carry an individual signature. To achieve this,
we longitudinally recorded the cries of premature babies
born between 26 and 33 weeks from birth until their theoret-
ical term. We then assessed the changes in the acoustic char-
acteristics of these cries, as well as their ability to carry the
identity of the babies over time.

II. MATERIALS AND METHODS

A. Recordings of baby cries

We recorded two groups of babies—premature and full-term—using Audiomoth passive automatic recorders (sampling frequency: 48 kHz) (Hill et al., 2018), equipped with a connected MEMS microphone (Shah et al., 2019). These recorders are lightweight (approximately 90 g, including batteries) and compact (58 × 48 × 15 mm), and can continuously record for several days.

We recorded two groups of babies: premature and full
term. The premature babies were recruited from the Neonatal
Intensive Care Unit at Saint Etienne University Hospital.¹
Parents were informed of the registration protocol. Their signed
consent was not required by the *Comité de Protection des
Personnes*. We included 20 preterm infants (ten boys and ten
girls) born between 26 and 33 weeks of gestational age,
between November 2020 and May 2021. Infants intubated
from birth were included only after removal of the tube. The
recorder was placed in the incubator at a distance of 20 cm
from the baby's head. For very premature infants, the incuba-
tors were closed and heated, and the babies received
non-invasive respiratory assistance with a continuous positive
airway pressure (CPAP), bilevel positive airway pressure
(BiPAP), or high-flow nasal cannula (nHF) mode. Over time,
respiratory assistance could be removed and the incubators

opened. The 20 babies included were recorded between 24
and 72 h each week throughout their hospitalization. The
babies were recorded over a variable period, from 1 to 13
weeks, with an average of 6.23 weeks of recording.

The babies born at term were recruited during another
ongoing study conducted by our team (Baby Cry 1000
cohort; Authorization No. IDRCB 2024-A00286-41). The
group consisted of 25 newborns (18 boys and seven girls),
born between May 13 and June 11, 2024 (gestational age:
39.46 ± 1.23 weeks, 35.86–41.86 weeks). For each baby,
the recorder was placed on a stand approximately 1 m from
the baby. Recordings were performed no later than 3 days
after birth, between 5 pm and 8 am.

B. Extraction of crying sequences

As the premature babies were recorded in the Neonatal
Intensive Care Unit and the full-term babies in their matery-
nity rooms, the recording conditions were different and we
had to implement two adapted strategies to extract the
recording sequences containing the cries.

The recording environment of preterm babies was noisy,
mainly due to the non-invasive respiratory assistance used in
the Neonatal Intensive Care Unit or adult voices in the vicin-
ity. To limit the impact of this external noise on the recorded
signals, we manually extracted the cry sequences using visual
inspection of the spectrograms of each recording session
[PRAAT software (Boersma and van Heuven, 2001)]. We
extracted 794 crying sequences (mean duration of a sequence:
104 ± 86 s, minimum duration: 13 s, maximum duration:
297 s) from 60 recordings with an average duration of 48 h.
For 18 babies, we obtained between five and 221 cry sequen-
ces (mean duration 44 ± 52 s). We were only able to obtain a
single cry sequence for the last two babies and therefore
excluded them from the acoustic analysis.

The recordings contained less environmental noise for
the group of full-term babies, the main source of noise being
adults talk. We were therefore able to implement an auto-
matic strategy for extracting cry sequences using the
YamNet model (Plakal and Ellis, 2020). Based on the
MobileNet architecture (Howard et al., 2017), this neural
network predicts the probability of one of 521 audio events
in the AudioSet ontology (Gemmeke et al., 2017) for tempo-
ral windows of 0.96 s. We considered that a window was
valid, i.e., that it contained a baby cry with a good signal-to-
noise ratio, if the score of the “baby cry” event was greater
than 0.11 and the score of the “speech” event was less than
0.06. Windows advanced with 50% overlap, and several
consecutively validated windows allowed for extracting a
cry sequence. Each sequence was considered valid if the
mean score of the “baby cry” event across all sequence win-
dows was greater than 0.11 and the mean score of the
“speech” event was less than 0.06. We then performed a sec-
ond run through YamNet to eliminate false-positives or cry
sequences that we considered too noisy. For each sequence
extracted, we calculated two ratios: the ratio between the
number of windows in the sequence and the number of
windows for which the score associated with the “speech” event was greater than 0.8, and the ratio between the number of windows in the sequence and the number of windows for which the score associated with the “baby cry” event was greater than 0.2. Sequences for which the first ratio was less than 5% and the second was greater than 5% were retained; otherwise, they were deleted. This process yielded 3284 cry sequences (mean duration: 111.17 ± 10.871 s, 3.36–132 s).

### C. Acoustics analysis

We characterized the acoustic structure of cries by measuring the following ten key variables, which are considered highly informative in human and animal nonverbal communication (Charlton et al., 2020; Kreiman and Sidtis, 2011; Pisanski and Bryant, 2019), and have already been successfully applied to characterize acoustic variations in baby cries (Lockhart-Bouron et al., 2023).

(1) Median amount of energy in the upper harmonics (Harm energy) (dB): ratio between the total spectral energy above $125 \times F_0$ and the total spectral energy below $125 \times F_0$.

(2) Roughness (%): rapid fluctuations in amplitude occurring at frequencies between 30 and 150 Hz (Arnal et al., 2015). Roughness refers to the irregularity of the vocal source. High roughness results in a hoarse sound.

(3) Harmonic-to-noise ratio (HNR): a logarithmic metric related to the proportion of the intensity of the harmonic signal to noise (de Krom, 1993). The lower this ratio, the noisier the signal (HNR = 0 corresponds to a sound with as much energy in the harmonics as in the noise).

(4) Median spectral centroid (Hz): the center of gravity of the spectrum. This value indicates the amount of energy in high frequencies relative to low frequencies.

(5) Proportion of voiced frames (%): part of the vocalization produced by the vibration of the vocal cords.

(6) Median pitch $F_0$ (Hz): the opening and closing rate of the glottis. The geometry and mechanical properties of the vocal folds determine $F_0$.

(7) Median cepstral peak prominence (CPP) (dB): this feature corresponds to the ratio between the highest peak amplitude of the cepstrum and the linear regression line showing the average energy of the signal. CPP is used to quantify the “quality” of the voice and acoustic signal, and its degree of harmonicity in relation to the severity of dysphonia. Indeed, a periodic signal presents a prominent cepstral peak (i.e., distinct and of high amplitude) and high CPP values corresponding to a well-defined fundamental frequency and harmonic structure. In contrast, dysphonic vocal signals are associated with decreased cepstral peak amplitude (i.e., lower harmonic energy) and CPP values (Batthyany et al., 2019).

(8) Median Werner entropy: logarithm of the ratio between the geometric and arithmetic means of power spectral density values for different frequencies (Tchernichovski et al., 2000). Werner entropy characterizes the spectral

complexity of a signal, ranging from a single sine wave (pure tones) represented by an entropy value of 0, to white noise in which all frequencies are equally represented and which is characterized by a theoretical entropy value of 1.

(9) Peak frequency (Hz): the frequency having the highest energy in the spectral density.

(10) Duration of each crying syllable (ms) within the sequences.

We performed these acoustic measurements automatically using Soundgen software (Anikin, 2019), with a window length of 50 ms and 50% overlap.

### D. Statistical analysis

To assess changes in the acoustic structure of preterm infant cries, we built a linear mixed-effects model using the R lmer package (Bates et al., 2015). The gestational age of the baby was considered as the response variable, and the acoustic features of the cry as covariates (which were all previously standardized to a mean of 0 and a standard deviation of 1). We included a random intercept for each baby, as well as the weight and height of the baby, to control for these factors. The model structure was as follows:

$$ 
\begin{aligned}
\text{age} & \sim \text{Harm Energy} + \text{Roughness} + \text{HNR} \\
& + \text{Spectral Centroid} + \text{Voiced} + \text{Pitch} + \text{CPP} \\
& + \text{Entropy} + \text{Peak Frequency} + \text{Duration} \\
& + \text{Weight} + \text{Height} + (1 | \text{ID}).
\end{aligned}
$$

We first tested whether the cries of premature infants have the same characteristics as those of full-term infants once their gestational age corresponds to that of a full-term baby. We therefore analyzed the cries of preterm infants aged over 37 weeks and compared their acoustic characteristics with those of full-term infants using a multivariate analysis of variance (MANOVA).

Finally, we divided the preterm group into two subgroups (under 36 weeks and over 36 weeks) to test the emergence and stabilization of an individual vocal signature in cries. We set the cutoff at 36 weeks to ensure a sufficient number of cries and babies in both groups. We therefore ensured that there was an equal number of babies in both groups, and kept for this analysis only those babies for whom we had recordings before and after 36 weeks. Each group was then split into training and test datasets. We trained a random forest model, using the randomForest R package (Liaw and Wiener, 2002), to predict the identity of the crying baby from the acoustic features of their cry for both age groups. We calculated the correct prediction rate on each test set, which allowed us to determine whether it is possible to extract an individual signature from the cry acoustics, and whether this individual signature changes and becomes more reliable over time in premature infants. In order to have an equal number of observations for each baby when training the model, and to correct for an unbalanced
<figure><img src="image_3.png" /><figcaption>FIG. 1. The acoustic structure of premature baby cries changes between birth and theoretical term. Example spectrograms of three cries of the same premature baby (50 ms Gaussian window, 50% overlap, frequency in kHz on a mel scale). We take cries of similar duration for a better visual comparison. The x-axis represents time, in seconds, the y-axis represents frequency, in Hertz, the coloration represents the amplitude. Each cry has been recorded at a different age, illustrating changes in acoustic features over time. (a) 30 weeks, (b) 35 weeks, (c) 44 weeks. Age is postnatal gestational age. The cries of preterm infants at birth are marked by very short exhalation sequences, which increase over time, and become less noisy.</figcaption></figure>

distribution, we oversampled the data for the levels that required it, aiming for 50 observations per baby. The random forest model used 500 trees for each iteration.

### III. RESULTS

#### A. Evolution of the acoustic structure of preterm infant cries

We first analyzed the evolution of the acoustic structure of preterm infant cries as they approach their theoretical term (Fig. 1). The results are summarized in Table I. The chosen linear model explains a significant proportion of the variance in our data, as evidenced by a marginal $R^2$ of 0.867.

Baby weight and height, which are strongly correlated with age, clearly contribute to this high value, underlying that the characteristics of cries change as babies grow. After controlling for these two variables, we identified five acoustic descriptors showing significant changes over time (Fig. 2). In summary, cries become more tonal with age, with entropy decreasing [−0.11 per week, 95% confidence interval (CI) (−0.14, −0.07); Fig. 2(a)] and CPP increasing [+0.08 per week, 95% CI (0.02, 0.14); Fig. 2(b)]. Pitch

increases [+0.08 per week, 95% CI (0.05, 0.11); Fig. 2(c)], as does duration [+0.03 per week, 95% CI (0.01, 0.06)]. As shown in Fig. 2(d), the proportion of vocalized frames increased sharply, particularly around week 33. This result is not identified by our first statistical model, but becomes significant [+1.48, 95% CI (1.04, 1.93)] if we add the slope as a random effect, in addition to the intercept for each subject (this avoids Simpson's paradox),

$$ \text{gestational age Voiced} + (\text{Voiced} | \text{ID}). $$

#### B. Difference between the cries of preterm and full-term babies

We then tested whether the changes in the acoustic structure of premature baby cries during their growth led to cries similar to those of full-term infants (Fig. 3). Using a MANOVA, we compared the ten acoustic descriptors of the preterm infant cries who had reached their theoretical term with those of full-term infants (see Sec. II for a detailed description). A visual analysis of the distribution of the ten features we calculated to describe the cries illustrates these differences and their magnitude (Fig. 4).

The MANOVA reveals a significant difference between the two groups [$F(12, 3628) = 1061.3, p < 0.0001$]. Except for the variable representing the amount of energy in the upper harmonics [Fig. 4(a)], all acoustic descriptors significantly differ between the two groups. Roughness, HNR, and spectral centroid are higher for full-term infants [Figs. 4(b)–4(d)]. Conversely, the proportion of voiced frames are higher for premature infants [Fig. 4(e)]. Differences are smaller for pitch and CPP, with higher values for premature and full-term infants, respectively [Figs. 4(f) and 4(g)]. The entropy and peak distributions overlap considerably, with higher entropy for premature infants and higher peak frequency for full-term infants [Figs. 4(h) and 4(i)].

These differences indicate that, despite the developmental trajectory of premature infants and the joint evolution of the acoustic structure of their cries, their changes along with the baby's growth is not sufficient to reach the characteristics of the cries of full-term babies. On the other hand, the cries of preterm babies are less rough, have a higher pitch,

<table><caption>TABLE I. Linear mixed-effect regression results for premature infants, with age as response variable and acoustic descriptors as covariates. Weight and height are added as controls. A random intercept per child is used.</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p</th></tr></thead><tbody><tr><td>(Intercept)</td><td>35.92</td><td>[35.25, 36.29]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Harm energy</td><td>0.02</td><td>[-0.02, 0.07]</td><td>0.339</td></tr><tr><td>Roughness</td><td>-0.02</td><td>[-0.06, 0.02]</td><td>0.268</td></tr><tr><td>HNR</td><td>-0.00</td><td>[-0.06, 0.06]</td><td>0.987</td></tr><tr><td>Spectral centroid</td><td>0.04</td><td>[-0.03, 0.11]</td><td>0.227</td></tr><tr><td>Voiced</td><td>-0.08</td><td>[-0.14, -0.03]</td><td><strong>0.004</strong></td></tr><tr><td>Pitch</td><td>0.08</td><td>[0.05, 0.11]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>CPP</td><td>0.08</td><td>[0.02, 0.14]</td><td><strong>0.006</strong></td></tr><tr><td>Entropy</td><td>-0.11</td><td>[-0.14, -0.07]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Peak frequency</td><td>-0.04</td><td>[-0.08, 0.00]</td><td>0.061</td></tr><tr><td>Duration</td><td>0.03</td><td>[0.01, 0.06]</td><td><strong>0.020</strong></td></tr><tr><td>Weight</td><td>2.78</td><td>[2.63, 2.93]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Height</td><td>1.06</td><td>[0.89, 1.22]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Observations</td><td>791</td><td></td><td></td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td colspan="3">0.867/0.993</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>FIG. 2. Variation over time in cry acoustics descriptors in preterm infants. The x-axis represents the child's postnatal age, in weeks. (a) Cry entropy, (b) cepstral peak prominence, (c) pitch, (d) proportion of voiced frames. See Sec. II C for a detailed description of variables.</figcaption></figure>

and a higher proportion of voiced frames than those of full-term babies.

We explain the positive correlation between HNR and roughness by the differences in recordings conditions: background noises were more present in the premature cries' recordings (see Sec. II B). Whereas roughness, computed on voiced frames, is lower for premature babies, indicating less rough cries, HNR is also lower for premature babies,

because the recording environment is noisier (e.g., non-invasive respiratory assistance).

### C. Stabilization of individual signature in preterm baby cries

Finally, we assessed the presence of an individual acoustic signature in the cries of premature babies and its stabilization during ontogeny. Using the same ten acoustic

<figure><img src="image_5.png" /><figcaption>FIG. 3. Differences between cries of full-term and preterm babies persist after preterm babies reach their theoretical term. Example spectrograms of two cries (50 ms Gaussian window, 50% overlap, frequency in kHz on a mel scale). The x-axis represents time, in seconds, the y-axis represents frequency, in Hertz, the coloration represents the amplitude. (a) Spectrogram of a preterm baby's cry older than 37 weeks (in postnatal gestational age), (b) spectrogram of a full-term baby's cry. Despite the evolution of the preterm cries, which become more tonal with age as the baby approaches its theoretical term; cries of the full-term baby have less energy in high frequencies, and are more tonal with clearer harmonics.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>FIG. 4. Comparison of acoustic cry features between preterm and full-term babies. Comparison of acoustic cry features between preterm and full-term babies. All the features, except (a), the quantity of energy in the harmonics, are statistically different between the two groups [MANOVA, $F(12, 3628) = 1061.3, p < 0.0001$]. (a) The quantity of energy in the harmonics, (b) roughness, (c) HNR, (d) spectral centroid, (e) proportion of voiced frames, (f) pitch, (g) cepstral peak prominence, (h) entropy, (i) peak frequency. See Sec. II C for a detailed description of variables.</figcaption></figure>

descriptors, we tested the ability of a classification model (random forest) to predict the identity of a crying baby for two sets of cries: cries recorded when preterm babies were less than 36 weeks postgestational and cries recorded when babies were more than 36 weeks postgestational (see Sec. II D for a detailed description of methods).

We observed that the individual acoustic signatures of preterm infants stabilize over time as they approach their theoretical term. The random forest model achieved an accuracy of 74.24% when the babies were less than 36 weeks old. This accuracy reached 81.25% when the babies were more than 36 weeks old (Fig. 5). This 7.01 point increase in accuracy corresponds to a 27.21% reduction in errors, indicating a stabilization of the individual signature in the acoustics of preterm infant cries.

## IV. DISCUSSION

Our analysis of nearly 800 cries from 18 preterm babies, born between 26 and 33 weeks and recorded

longitudinally until their theoretical term, reveals that their cries change as they approach term. Their acoustic structure becomes increasingly similar to that of the cries of full-term babies. In particular, the cries of premature babies have an individual acoustic signature that stabilises over time. However, these cries are still marked by a certain number of specific characteristics, which means that premature babies can still be distinguished vocally from full-term babies at the time of their theoretical term.

### A. Premature baby cries change over time

The acoustic structure of premature baby cries changes between birth and theoretical term. Cepstral peak prominence (CPP) increases, while acoustic entropy decreases. These changes indicate that cries become less noisy and more harmonious. In other words, whereas the cries of premature babies are initially marked by a predominance of non-linear acoustic phenomena (chaos perceived as quasi-white noise), they gradually acquire a distinct fundamental
<figure><img src="image_7.png" /><figcaption>FIG. 5. Individual signature in baby cries. Confusion matrices show by coloring cell (i,j) the conditional probability of guessing that a cry came from baby j when, in fact, it was emitted by baby i. (a) before 36 weeks of gestational age, (b) after 36 weeks of gestational age (see Methods for details).</figcaption></figure>

frequency accompanied by its harmonic series. Cries also become longer.

In addition to being increasingly distinct, $F_0$ is increasingly high. On the one hand, weight gain and child growth should reduce $F_0$. On the other hand, improved control of the laryngeal muscles enables babies to produce cries with a higher $F_0$ (Zhang, 2016). $F_0$ plays a significant role in the perception of the level of distress (Esposito et al., 2015; Kelly et al., 2017). We hypothesize that children acquire greater control over vocal production and may intentionally produce cries with higher $F_0$ to elicit caregiving behavior from parents. This increase signs a better physiological and neurological control and maturity (Rautava et al., 2007; Shinya et al., 2017).

These results are in line with initial explorations reported in the literature (Michelsson et al., 1983; Gilbert and Robb, 1996; Baeck and Souza, 2007). The developmental trajectory of cries in premature babies is reminiscent of that seen in full-term infants during the first 3 months of life (Lockhart-Bouron et al., 2023). Here, too, cry energy is increasingly concentrated in $F_0$, reflecting increasingly tonal cries, and increasingly higher, reflecting significant physiological changes. These changes suggest the gradual acquisition of better control of the buco-phonatory apparatus.

## B. Individual acoustic signature despite prematurity

Previous studies have shown that baby cries carry individual acoustic signatures. These signatures enable parents or any trained person to identify a baby from their cries (Gustafsson et al., 2013; Bouchet et al., 2020; Lockhart-Bouron et al., 2023). A recent study in full-term babies shows that this vocal signature is particularly pronounced in cries expressing discomfort (recorded during a bathing session) and becomes less prominent in cries expressing strong pain (recorded during a vaccination session) (Corvin et al., 2024). This suggests that cries marked by non-linear acoustic phenomena, characteristic of the expression of pain

(Massenet et al., 2025) present fewer idiosyncratic features. In the present study, we show that this individual acoustic signature also exists in the cries of preterm infants. Every premature baby thus cries distinctively from an early age. However, the signature becomes more reliable over time, accompanying structural changes in the cries, which become increasingly more harmonious and increasingly less noisy.

## C. A persistent difference with full-term babies

Although preterm infants have a greater postnatal experience and develop physiological control during this period, they do not fully catch up to full-term infants in terms of acoustic structure by the time they reach their theoretical term. This observation does not imply a developmental "hang-up" (Michelsson et al., 1983) but rather aligns with more recent literature, which shows a persistent difference in acoustic structure at term (Orlandi et al., 2016).

On the other hand, we have shown that the cries of full-term infants are rougher, and those of premature infants are more voiced and have a higher pitch. This finding supports the notion of more mature motor vocal control in preterm infants, particularly those born with average precocity (gestational age between 32 and 37 weeks) (Shinya et al., 2017). This difference has implications, as a higher $F_0$ is observed in preterm infants compared to full-term children 1.5 years later (Rautava et al., 2007). The observed acoustic differences indicate that while prematurity poses a significant stressor for the child, it also serves as an additional experience that facilitates the development of enhanced vocal control.

## D. Limitations

Our study has some limitations. The first one is the sample size and the non-uniform distribution of cries among the children. The sample comprised 18 preterm babies, which
were chosen to ensure high-quality recordings and to enable longitudinal tracking. However, not all babies cry with the same frequency or intensity, and variations in precocity, as well as the need to discard some cries for quality reasons, resulting in an uneven distribution of crying data. While we have controlled for these factors in our statistical modeling, replicating this analysis with a larger sample would be beneficial, especially given the individual variability in crying behavior. Additionally, we could not follow up with the recorded babies at a later stage to assess their neurodevelopment further. Future studies with a larger sample size could explore this by tracking these babies and evaluating their abilities, particularly their language skills, as they grow older. In comparing the premature and full-term groups, it is important to acknowledge that the recording conditions differed between them. While we applied specific sequence extraction strategies to mitigate the effects of these differences, variations in the recording environments remain and represent a limitation of the present study.

While several studies about full-term infants show that parents are able to recognize their child from their cries (Gustafsson et al., 2013; Bouchet et al., 2020; Lockhart-Bouron et al., 2023; Corvin et al., 2024), we were unable to assess parents' ability to do so with their preterm infants. We employed a classification model using acoustic features to determine whether there is an individual signature in cries, and the model was able to identify which baby produced the cry based on these parameters. However, it would be valuable to explore whether parents can recognize their preterm infants from their cries before they reach term. This is particularly complex because preterm infants are often in a medical environment where parents may not be present at all times. Furthermore, this separation, which must be limited, could have an impact on the ability of parents to identify their own child's cries, given the importance of experience and time spent with their baby in this process of recognition and discrimination (Bouchet et al., 2020).

## V. CONCLUSION

In conclusion, the present study suggests that preterm infants develop physiological and neurological control from birth to their theoretical term, as reflected in the acoustics of their cries. During this period, the noisy components of their cries decrease, and the $F_0$ increases, even as they grow and gain weight, indicating better control of vocal production. Similarly to full-term babies, an individual signature is present in their cries, becoming more pronounced as they approach term and their motor control becomes more refined. However, despite this developmental trajectory, the cry acoustics in preterm infants who have reached their theoretical term remain distinct from those of full-term infants, which remain less dysphonic with fewer noisy components.

## ACKNOWLEDGMENTS

Authorization was obtained from the Comité de Protection des Personnes to conduct audio recordings, No.

IRBN212020/CHUSTE for premature baby cries, and No. IDRCB 2024-A00286-41 for the full-term babies. Funding was provided by the ANR (BABYCRY project and SCREAM project), the Mécénat des Mutuelles AXA (N.M.), the LABEX CELYA (France), the University of Saint-Etienne (France), the Centre National de la Recherche Scientifique (CNRS, France), the Institut National de la Santé et de la Recherche Médicale (Inserm, France), and the Institut Universitaire de France (N.M. and D.R.).

## AUTHOR DECLARATIONS

### Conflict of Interest

The authors have no conflicts to disclose.

## DATA AVAILABILITY

The data that support the findings of this study will be openly available, following the submission of a data paper.

<footnote n="1">¹Authorization No. IRBN212020/CHUSTE by the Comité de Protection des Personnes, registered at https://clinicaltrials.gov/ under No. NCT04522414.</footnote>

<footnote>Anikin, A. (2019). "Soundgen: An open-source tool for synthesizing non-verbal vocalizations," *Behav. Res.* **51**(2), 778-792.</footnote>

<footnote>Arnal, L. H., Flinker, A., Kleinschmidt, A., Giraud, A.-L., and Poeppel, D. (2015). "Human screams occupy a privileged niche in the communication soundscape," *Curr. Biol.* **25**(15), 2051-2056.</footnote>

<footnote>Baeck, H. E., and de Souza, M. N. (2007). "Longitudinal study of the fundamental frequency of hunger cries along the first 6 months of healthy babies," *J. Voice* **21**(5), 551-559.</footnote>

<footnote>Barbier, G., Boë, L.-J., Captier, G., and Laboissière, R. (2015). "Human vocal tract growth: A longitudinal study of the development of various anatomical structures," in *Interspeech 2015*, pp. 364-368.</footnote>

<footnote>Barr, R. G., Hopkins, B., and Green, J. A. (2000). *Crying as a Sign, a Sympton, & a Signal: Clinical Emotional and Developmental Aspects of Infant and Toddler Crying* (Cambridge University Press, New York), pp. viii and 228.</footnote>

<footnote>Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015). "Fitting linear mixed-effects models using lme4," *J. Stat. Softw.* **67**(1), 1-48.</footnote>

<footnote>Batthyany, C., Maryn, Y., Trauwaen, I., Caelenberghe, E., van Dinther, J., Zarowski, A., and Wuyts, F. (2019). "A case of specificity: How does the acoustic voice quality index perform in normophonic subjects?," *Appl. Sci.* **9**(12), 2527.</footnote>

<footnote>Boersma, P., and van Heuven, V. (2001). "PRAAT, a system for doing phonetics by computer," *Glot Int.* **5**(9/10), 341-347.</footnote>

<footnote>Bouchet, H., Plat, A., Levréro, F., Reby, D., Patural, H., and Mathevon, N. (2020). "Baby cry recognition is independent of motherhood but improved by experience and exposure," *Proc. R. Soc. B.* **287**(1921), 20192499.</footnote>

<footnote>Cabon, S., Met-Montot, B., Poree, F., Rosec, O., Simon, A., and Carrault, G. (2021). "Automatic extraction of spontaneous cries of preterm newborns in neonatal intensive care units," in *2020 28th European Signal Processing Conference (EUSIPCO)* (IEEE, Amsterdam, Netherlands), pp. 1200-1204.</footnote>

<footnote>Charlton, B. D., Pisanski, K., Raine, J., and Reby, D. (2020). "Coding of static information in terrestrial mammal vocal signals," in *Coding Strategies in Vertebrate Acoustic Communication Series Title: Animal Signals and Communication* (Springer International Publishing, Cham, Switzerland), Vol. 7, pp. 115-136.</footnote>

<footnote>Corvin, S., Fauchon, C., Patural, H., Peyron, R., Reby, D., Theunissen, F., and Mathevon, N. (2024). "Pain cues override identity cues in baby cries," *iScience* **27**(0), 110375.</footnote>

<footnote>de Krom, G. (1993). "A cepstrum-based technique for determining a harmonics-to-noise ratio in speech signals," *J. Speech. Lang. Hear. Res.* **36**(2), 254-266.</footnote>
Esposito, G., Hiroi, N., and Scattoni, M. L. (2017). "Cry, baby, cry: Expression of distress as a biomarker and modulator in autism spectrum disorder," *Int. J. Neuropsychopharmacol.* **20**(6), 498–503.

Esposito, G., Nakazawa, J., Venuti, P., and Bornstein, M. H. (2015). "Judgment of infant cry: The roles of acoustic characteristics and sociodemographic characteristics," *Jpn. Psychol. Res.* **57**(2), 126–134.

Esposito, G., and Venuti, P. (2009). "Comparative analysis of crying in children with autism, developmental delays, and typical development," *Focus Autism Other Dev. Disabl.* **24**(4), 240–247.

Fuhr, T., Reetz, H., and Wegener, C. (2015). "Comparison of supervised-learning models for infant cry classification /Vergleich von Klassifikationsmodellen zur Säuglingsschreianalyse," *Int. J. Health Prof.* **2**(1), 4–15.

Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., and Ritter, M. (2017). "Audio set: An ontology and human-labeled dataset for audio events," in *2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pp. 776–780.

Gilbert, H. R., and Robb, M. P. (1996). "Vocal fundamental frequency characteristics of infant hunger cries: Birth to 12 months," *Int. J. Pediatr.* **Otorhinolaryngol.** **34**(3), 237–243.

Gustafsson, E., Levréro, F., Reby, D., and Mathevon, N. (2013). "Fathers are just as good as mothers at recognizing the cries of their baby," *Nat. Commun.* **4**(1), 1698.

Hammoud, M., Getahun, M. N., Baldycheva, A., and Somov, A. (2024). "Machine learning-based infant crying interpretation," *Front. Artif. Intell.* **7**, 1337356.

Hariharan, M., Sindhu, R., and Yaacob, S. (2012). "Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network," *Comput. Methods Programs Biomed.* **108**(2), 559–569.

Hartnick, C. J., Rehbar, R., and Prasad, V. (2005). "Development and maturation of the pediatric human vocal fold lamina propria," *Laryngoscope* **115**(1), 4–15.

Hill, A. P., Prince, P., Piña Covarrubias, E., Doncaster, C. P., Snaddon, J. L., and Rogers, A. (2018). "AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment," *Methods Ecol. Evol.* **9**(5), 1199–1211.

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). "MobileNets: Efficient convolutional neural networks for mobile vision applications," *arXiv:1704.04861*.

Ji, C., Mudiyanselage, T. B., Gao, Y., and Pan, Y. (2021). "A review of infant cry analysis and classification," *J. Audio. Speech. Music Proc.* **2021**(1), 8.

Kelly, T., Reby, D., Levréro, F., Keenan, S., Gustafsson, E., Koutseff, A., and Mathevon, N. (2017). "Adult human perception of distress in the cries of bonobo, chimpanzee, and human infants," *Biol. J. Linn. Soc.* **120**(4), 919–930.

Kheddache, Y., and Tadj, C. (2019). "Identification of diseases in newborns using advanced acoustic features of cry signals," *Biomed. Signal Process. Control* **50**, 35–44.

Khozaei, A., Moradi, H., Hosseini, R., Pouretemad, H., and Eskandari, B. (2020). "Early screening of autism spectrum disorder using cry features," *PLoS One* **15**(12), e0241690.

Koutseff, A., Reby, D., Martin, O., Levrero, F., Patural, H., and Mathevon, N. (2018). "The acoustic space of pain: Cries as indicators of distress recovering dynamics in pre-verbal infants," *Bioacoustics* **27**(4), 313–325.

Kreiman, J., and Sidtis, D. (2011). *Foundations of Voice Studies: An Interdisciplinary Approach to Voice Production and Perception* (Wiley-Blackwell, Malden, MA).

Laitman, J. T., Heimbuch, R. C., and Crelin, E. S. (1978). "Developmental change in a basicranial line and its relationship to the upper respiratory system in living primates," *Am. J. Anat.* **152**(4), 467–482.

Laitman, J. T., and Reidenberg, J. S. (1993). "Specializations of the human upper respiratory and upper digestive systems as seen through comparative and developmental anatomy," *Dysphagia* **8**(4), 318–325.

Lawford, H. L. S., Sazon, H., Richard, C., Robb, M. P., and Bora, S. (2022). "Acoustic cry characteristics of infants as a marker of neurological dysfunction: A systematic review and meta-analysis," *Pediatr. Neurol.* **129**, 72–79.

Liwai, A., and Wiener, M. (2002). "Classification and regression by random Forest," *R News* **2**(3), 18–22, available at https://CRAN.R-project.org/doc/Rnews/.

Lockhart-Bouron, M., Anikin, A., Pisanski, K., Corvin, S., Cornec, C., Papet, L., Levréro, F., Fauchon, C., Patural, H., Reby, D., and Mathevon, N. (2023). "Infant cries convey both stable and dynamic information about age and identity," *Commun. Psychol.* **1**(1), 26.

Massenet, M., Pisanski, K., Reynaud, K., Mathevon, N., Reby, D., and Anikin, A. (2025). "Acoustic context and dynamics of nonlinear phenomena in mammalian 2 calls: The case of puppy whines," *Philos. Trans. R. Soc. B: Biol. Sci.* **380**(1923), 20240022.

Michelsson, K., Järvenpää, A.-L., and Rinne, A. (1983). "Sound spectrographic analysis of pain cry in preterm infants," *Early Human Develop.* **8**(2), 141–149.

Onu, C. C., Latremouille, S., Gorin, A., Wang, J., Udeogu, I., Ekwochi, U., Ubuane, P. O., Kehinde, O. A., Salisu, M. A., Briggs, D., Bengio, Y., and Precup, D. (2023). "A cry for help: Early detection brain injury newborns," *arXiv:2310.08338* [cs, eess, q-bio].

Orlandi, S., Garcia, C. A. R., Bandini, A., Donzelli, G., and Manfredi, C. (2016). "Application of pattern recognition techniques to the classification of full-term and preterm infant cry," *J. Voice* **30**(6), 656–663.

Orlandi, S., Manfredi, C., Bocchi, L., and Scattoni, M. L. (2012). "Automatic newborn cry analysis: A non-invasive tool to help autism early diagnosis," *Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.* **2012**, 2953–2956.

Pisanski, K., and Bryant, G. A. (2019). "The evolution of voice perception," in *The Oxford Handbook of Voice Studies*, edited by N. S. Eidsheim and K. Meizel (Oxford University Press, Oxford, UK), pp. 268–300.

Plakal, M., and Ellis, D. (2020). "TensorFlow Hub," https://tfhub.dev/google/yamnet/1.

Rautava, L., Lempinen, A., Ojala, S., Parkkola, R., Rikalainen, H., Lapinleimu, H., Haataja, L., and Lehtonen, L. (2007). "Acoustic quality of cry in very-low-birth-weight infants at the age of 1 1/2 years," *Early Human Develop.* **83**(1), 5–12.

Reby, D., Levréro, F., Gustafsson, E., and Mathevon, N. (2016). "Sex stereotypes influence adults' perception of babies' cries," *BMC Psychol.* **4**(1), 19.

Schönweiler, R., Kaese, S., Möller, S., Rinscheid, A., and Ptok, M. (1996). "Neuronal networks and self-organizing maps: New computer techniques in the acoustic evaluation of the infant cry," *Int. J. Pediatr. Otorhinolaryngol.* **38**(1), 1–11.

Shah, M. A., Shah, I. A., Lee, D.-G., and Hur, S. "Design approaches of MEMS microphones for enhanced performance," *J. Sensors.* **2019**, 9294528.

Sheinkopf, S. J., Iverson, J. M., Rinaldi, M. L., and Lester, B. M. (2012). "Atypical cry acoustics in 6-month-old infants at risk for autism spectrum disorder," *Autism Res.* **5**(5), 331–339.

Shinya, Y., Kawai, M., Niwa, F., Imafuku, M., and Myowa, M. (2017). "Fundamental frequency variation of neonatal spontaneous crying predicts language acquisition in preterm and term infants," *Front. Psychol.* **8**, 291209.

Tchernichovski, O., Nottebohm, F., Ho, C. E., Pesaran, B., and Mitra, P. P. (2000). "A procedure for an automated measurement of song similarity," *Anim. Behav.* **59**(6), 1167–1176.

Wermke, K., Robb, M. P., and Schlüter, P. J. (2021). "Melody complexity of infants' cry and non-cry vocalisations increases across the first six months," *Sci. Rep.* **11**(1), 4137.

Zeifman, D. M. (2001). "An ethological analysis of human infant crying: Answering Tinbergen's four questions," *Dev. Psychobiol.* **39**(4), 265–285.

Zhang, Z. (2016). "Mechanics of human voice production and control," *J. Acoust. Soc. Am.* **140**(4), 2614–2635.