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# A scoping review of the use of bioacoustics to assess various components of farm animal welfare

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Mathilde Coutant<sup>a, *</sup>, Avelyne S. Villain<sup>b</sup>, Elodie F. Briefer<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Bioacoustics

Vocalization

Animal welfare

Animal behaviour

Precision livestock farming

## ABSTRACT

Recording and analyzing animal sounds, including vocal and non-vocal cues, is a relevant tool to access and monitor different aspects of animal physiology, behaviour and general welfare in a remote, non-invasive and continuous fashion. This field, known as bioacoustics, is considerably growing, notably due to the development of machine learning techniques. Yet, applications of these methods in animal husbandry remain limited, and their potential may not be fully acknowledged. This review aims to present the potential of bioacoustics tools to monitor various aspects of the welfare of farm animals, and focuses on studies published since 2010. The first section of the review introduces the concept of bioacoustics, describes the different parameters that can be extracted from the sounds of animals, and explains how they can be analysed. The potential of these measures to inform about the physical characteristics, individuality, health, physiology, behaviour, and affective states of different species of farm animals are then presented, through a contemporary overview of the research field. Although this analysis identifies a variety of applications of bioacoustics tools in relation to the assessment and monitoring of farm animal welfare, it also points out the current scarcity of methods implemented in practice and available to farm management. Barriers to the development of bioacoustics measures in conventional farming are also discussed, and future directions for this field of research are suggested.

## 1. Introduction

The assessment and monitoring of animal welfare is an essential aspect of farm animal management. Animal welfare describes a measurable quality of a living animal at a particular time and context and may be considered as an integrative view of both physiological and mental health. It is not only dependent on the absence of disease, pain, distress or behavioural abnormalities, but also on the extent to which the animal is able to express species-specific behaviours and positive affect. The science of animal welfare is thus multidisciplinary, ranging from physiology to ethology (Broom, 2011). While traditional methods for monitoring welfare parameters rely on visual observations and clinical examinations, the need for non-invasive automated methods to monitor large groups of animals has, in the recent years, brought interest to novel techniques, such as bioacoustics. Bioacoustics is a multidisciplinary field that focuses on the study of animal communication (production, transmission and perception). Bioacoustics relies on both hardware (recording devices and microphones during data collection) and software (signal processing, modelling, classifying - using machine learning

algorithms for instance) to analyse and interpret the sounds produced by animals. The field of bioacoustics primarily emerged to study wild animal communication. In decades, acousticians, biologists and conservation scientists have achieved great progress in detecting, analysing, and interpreting animal sounds. Such methods can now reveal crucial information about the producers of the sounds, such as their individuality, physical characteristics, health, behaviour and affective states, which are, in turn, useful to assess their welfare (Manteuffel et al., 2004; McLaughlin et al., 2019).

Animal vocalizations have long been recognized as a biologically relevant tool to access information regarding the status of animals, including their emotions (Briefer, 2012), defined as multicomponent responses (behavioural, neurophysiological and cognitive) to specific stimuli of importance for the organism (Paul and Mendl, 2018). Vocalizations are indeed considered as expressions of distinctive inner states (Manteuffel et al., 2004). Besides emotional states, the structural characteristics of animal vocalizations can also be affected by their physiological and health status, as changes in respiratory function, heart rate, or salivation (among others) affect vocal production and the resulting

<footnote><sup>a</sup> Department of Animal and Veterinary Science, Aarhus University, Blichers Allé 20, Tjele 8830, Denmark</footnote>

<footnote><sup>b</sup> Behavioural Ecology Group, Section for Ecology & Evolution, Department of Biology, University of Copenhagen, Copenhagen Ø 2100, Denmark</footnote>

<footnote>* Corresponding author.
E-mail address: mathilde.coutant@anivet.au.dk (M. Coutant).</footnote>
vocal parameters (Briefer, 2012). Similarly, as body characteristics, including lung capacity, size of the trachea, or nasal morphology, impact on how the call is produced, markers of more ‘static information’, such as an animal body size, sex, or individuality can be traced in their vocalizations (Bowling et al., 2017; Green et al., 2019). Finally, behavioural patterns, such as sexual or feeding behaviour, also result in specifical calls, the singularity of which can be used to detect these behaviours (e.g., Aydin and Berckmans, 2016). In addition to vocalizations, other sounds, such as coughing or chewing noises, can reveal information about health and behaviour, and be highly relevant for assessing welfare (e.g. Ferrari and al., 2008).

Acoustic analyses present the advantages of being a non-invasive, cost-effective, and reliable alternative for monitoring husbandry animals. Based on sounds, elements of animal health, behaviour and hence welfare can be recorded remotely, continuously and automatically, providing real-time information on the animals' condition. Moreover, like other precision livestock farming tools, bioacoustics analyses have the potential to save on veterinary costs and improve livestock welfare by detecting early signs of disease or injury (Banhazi and Black, 2009).

Despite the recent expansion of the use of bioacoustics analyses in the field of conservation science for monitoring and studying wild animals, their application in animal husbandry remains comparatively limited, and their potential to monitor farm animal welfare has yet to be fully explored (McCloughlin et al., 2019). In addition, technical challenges in relation to recording sound in practice and individually identifying animals remain. In 2004, Manteuffel, Puppe and Schön presented a review on the relevance of vocalization analyses to assess animal welfare in pig, cattle and poultry. Since then, in almost two decades, technological improvements, especially in the field of bioengineering and machine learning algorithms, have led to the publications of numerous studies, exploring the automatic detection and monitoring of various aspects of livestock health, behaviour and welfare. This review therefore aims to provide the reader with a contemporary overview of the different aspects of farm animal conditions that can be recorded using vocal analyses, focusing on physiological, health, behavioural and affective indicators. The current development status for each indicator, i.e., whether an algorithm to automatically detect the indicator has been developed, validated, and published, will be reported. The review will also address limits of acoustic analyses, especially in the context of conventional housing and management, providing points of attention for future studies.

## 2. Vocal parameters recorded in farm animals signals

Before delving into how vocal indicators can inform about various aspects of farm animal health and welfare, this chapter aims to provide the reader with a brief summary of the mechanisms of vocal production in animals, the types of parameters that can be extracted from animal vocalizations, and the methods by which they can be analysed and classified. Importantly, this section does not provide an in-depth guide to performing signal processing analyses leading to the extraction of quantitative parameters from vocalizations, but rather an overview of the general approach to acoustic analyses. For more details on statistical methods to analyse calls, see for example Sueur (2018).

### 2.1. Classification of animal vocalizations

Non-human vocalizations can be broadly classified as *calls*, which are relatively short and simple (i.e. composed of one or a few sound units), and *songs*, which are longer and more complex (i.e. composed of several sound units arranged following a particular syntax; Bradbury and Vehrencamp, 2011). Although songs received a great interest due to their complexity, they are produced in a limited number of contexts (mostly territorial defence and mate attraction; Catchpole and Slater, 2008; Loning et al., 2023). However, calls are more widespread and have multiple functions (e.g. begging, alarm, contact). Often, their acoustic

structure and rate of occurrence within a time period (also referred to as call rate) will differ depending on the function or context of production (e.g. dog barks and growls). Species will therefore often produce several call types, constituting their call *repertoire*. For instance, pigs have a repertoire of five main call types (low and high grunts, croaks, squeals and scream; Tallet et al., 2013), cattle have been suggested to produce six main call types (Kiley, 1972), while horses emit three main types of vocal sounds (whinnies, nickers and squeals), in addition to some non-vocal ones (e.g. snort, snore and blow; Yeon, 2012). Poultry, in comparison, have a large repertoire of vocalizations, with over 30 types of calls described for adult and juvenile chickens (Manteuffel et al., 2004). These calls will, in most cases, not be produced alone, but within a sequence where the same call type is repeated or few call types are mixed (e.g. pig 'grunt-squeals' or 'grunt-screams').

### 2.2. Biological mechanisms of sounds production

In humans, speech is produced through the processes of respiration, phonation, resonance and articulation (Fant, 1970; Juslin and Scherer, 2008; Titze and Martin, 1998). The lungs generate an airflow, which then passes through the larynx. In the larynx, the airflow is converted into sound by vibration of the vocal folds. Then, this sound passes through the supralaryngeal vocal tract (pharynx, oral and nasal cavities), before radiating into the environment through the lips and nostrils. Therefore, three systems are involved in the production of speech. According to the source-filter theory of voice production (Fant, 1970; Titze and Martin, 1998), the interplay between these systems will shape the characteristics of the vocalizations in the time, frequency and amplitude domains.

While most mammals produce sounds through this same process, birds, and therefore poultry, slightly differ. Although they have a larynx, birds produce sounds through the syrinx, a vocal organ located at the end of the trachea. In these species, sound is hence not produced by vibration of the vocal folds, but through vibrations of the membrane walls of the syrinx (Riede et al., 2019).

The respiratory system (respiration process) includes the lungs and determines the *duration*, *rate* and *amplitude* of the voice. It also influences the fundamental frequency of the voice (hereafter $f_0$; lower frequency of a sound) through changes in the subglottal pressure. The phonatory system (phonation process), including the larynx and laryngeal structures (or syrinx in birds), defines the *source*. The vibration of the vocal folds determines $f_0$ characteristics and its harmonics (multiples of $f_0$). The anatomy and physiology of the larynx (vocal fold length, thickness, mass, tension and internal structure, i.e. collagen and elastin fibre densities and orientations), or syrinx, then determine the characteristics of $f_0$ (Fant, 1970).

Resonance and articulation processes involve the vocal tract, which is composed of all air cavities between the larynx and the opening of the mouth and nostril. The vocal tract acts as a *filter* and shapes the *source* signal by selectively amplifying certain frequencies (*formants*) and attenuating others. The number, height and the relative amplitude of these spectral peaks determine the *energy distribution* [i.e. distribution of energy in the spectrum (frequency vs time)] of the voice. The anatomy and physiology of the supralaryngeal vocal tract (e.g. shape and length of the vocal tract) influence how the *filter* acts and thus affects filter-related vocal parameters. The source-filter theory, initially developed in humans, has received validation and has been applied to numerous non-human animals, especially mammals and birds, due to homologous sound production systems (Taylor and Reby, 2010).

### 2.3. Acoustic features measured in the vocalizations

A large number of acoustics features may be extracted from a sound wave, from the temporal analysis of vocal sequences (number, rate, rhythms), to the fine analysis of each vocalization structure in the time (e.g. duration), frequency (e.g. $f_0$ and formants, energy distribution) and
amplitude (e.g. amplitude modulation) domain.

**Table 1** presents a non-exhaustive list of parameters that may be extracted from the calls of animals. Some of them are already well established, while others have received little attention so far. As the structure of the vocalization considered may affect the way we can extract some parameters, especially in the frequency domain, we also indicate how dependent on the vocalization structure (and thus the species) each parameter can be. Fig. 1 intends to provide visualization of some of these parameters.

We also report whether the parameters have already largely been used or not, how reliable they are and how structure specific they may be (i.e. whether they can be measured in any kind of vocalization or not).

Descriptors of vocal dynamic (A) and vocalization acoustic features (B-E) in time and frequency domains. (A) Example of an oscillogram (wave-form) of four goat bleats. Several descriptors may be extracted: the mean (±sd) inter-call interval, the mean (±sd) gap interval, the duty cycle (duration of the four bleats over the duration of the full sequence). B: Oscillogram of the first bleat, allowing inspection of an acceptable signal to background noise ratio. C: Envelop (absolute value of the waveform) of the chosen bleat with detection of start and end using a 5 % amplitude threshold for measuring bleat duration and visualization of amplitude modulations. D: Spectrogram (frequency as a function of time, color scale represents relative energy of certain frequencies, red being the maximum and blue the minimum, FFT wl = 1024 points, overlap 50 %). The spectrogram was plotted between 0 and Q90 % of the frequency distribution to allow better visualization (zoom in view). F1, F2, F3, F4 target the main formants of the bleat, as defined in Praat software [www.praat.org, (Boersma & Paul, 2001)]. E: Normalized spectrum (relative amplitude as a function of frequency) between Q5 % and Q90 % of the frequency distribution. Plots of several mean spectra computed using three different sizes of window (wl in points) for the FFT: wl = 512 in

dashed grey, wl = 1024 in solid grey, wl = 2048 in red, illustrating the degree of precision in the frequency domain. Descriptors of the frequency spectrum (Q25 %, Q50 %, Q75 %) and frequency peaks illustrating putative formants are illustrated in red. The functions and script to compute the analysis and the sound are available as supplementary material (Supplementary Script 1 and Supplementary Sound 1).

It is important to note that this list is non-exhaustive, as other types of parameters can be extracted from animal vocalizations, although these are generally less popular in studies of farm animals. For instance, statistical descriptors of mel frequency cepstrum coefficients, also referred to as cepstral coefficients, reflect compression of the acoustic data to extract information for the detection of phonetic differences, in a similar way to the human auditory system decomposes sounds to facilitate speech recognition (see Lyon and Ordubadi, 1982). Mcloughlin et al. (2019) also defined a list of other spectral analyses, including spectral centroid and spectral flux, used in other fields such as music research. Similarly, analyses based on the rhythm of vocalizations, inspired by music theory, can provide further descriptive parameters for a series of sounds (Ravignani and Norton, 2017). Shannon's entropy, estimating the diversity of call structures across large samples, can furthermore enable to estimate the ability of a call to encode information, especially in species with complex vocalizations (Shannon and Weaver, 1949; Freeberg and Lucas, 2012). These methods remain however, less common in bioacoustics analyses applied to the study of husbandry animals.

## 2.4. Obtaining acoustic features and analyzing acoustic data

### 2.4.1. Acoustic parameter extraction and analysis

Measurements in the temporal domain (on sequences of

<caption>**Table 1**</caption>

Acoustic features measurable in vocalizations or vocal sequences according to the sound domain (time, frequency, amplitude).

<table><thead><tr><th>Abbreviation</th><th>Name (if different from abbreviation)</th><th>Unit</th><th>Definition</th><th>How often measured ('-'= rarely; '+'= variable; '+'= often)</th><th>How structure specific ('-'= not specific, '+'= specific)</th></tr></thead><tbody><tr><td colspan="6"><strong>Descriptors of time series (descriptors of vocal behaviour)</strong></td></tr><tr><td>Rate</td><td>s-1</td><td></td><td>The number of vocalizations per time unit</td><td>++</td><td>-</td></tr><tr><td>Interval</td><td>Gap interval</td><td>s</td><td>Duration of silences between vocalizations across the time series</td><td>+</td><td>-</td></tr><tr><td>ICI</td><td>Inter-call interval</td><td>s</td><td>Duration between starts of subsequent vocalizations across the time series</td><td>+</td><td>-</td></tr><tr><td>Duty cycle</td><td>Duty cycle</td><td>-</td><td>Proportion of time vocalized across the duration of the time series</td><td>-</td><td>-</td></tr><tr><td colspan="6"><strong>Descriptors of each vocalization</strong></td></tr><tr><td colspan="6"><em>In the time domain</em></td></tr><tr><td>Duration</td><td>s-1</td><td></td><td>Duration of a vocalization (e.g. amplitude threshold detection)</td><td>++</td><td>-</td></tr><tr><td colspan="6"><em>In the frequency domain</em></td></tr><tr><td>Q25</td><td>First quartile</td><td>Hz</td><td>Frequency below which 25 % of the energy is contained</td><td>++</td><td>-</td></tr><tr><td>Q50</td><td>Median</td><td>Hz</td><td>Frequency below which 50 % of the energy is contained</td><td>++</td><td>-</td></tr><tr><td>Q75</td><td>Third quartile</td><td>Hz</td><td>Frequency below which 75 % of the energy is contained</td><td>++</td><td>-</td></tr><tr><td>IQR</td><td>Inter-quartile range</td><td>Hz</td><td>Frequency range between the third and the first quartile (indicates the bandwidth)</td><td>+</td><td>-</td></tr><tr><td>Fpeak</td><td>Frequency peak</td><td>Hz</td><td>Frequency of highest amplitude (also called the mode of the frequency distribution)</td><td>+</td><td>-</td></tr><tr><td>F1, F2, F3 etc...</td><td>Formant frequencies</td><td>Hz</td><td>Frequency value of each formant across the vocalization</td><td>+</td><td>+</td></tr><tr><td>f<sub>o</sub></td><td>Fundamental frequency</td><td>Hz</td><td>Frequency of the lowest harmonic across the vocalization</td><td>++</td><td>+</td></tr><tr><td>FM</td><td>Frequency modulation</td><td>s-1, Hz/ s or Hz</td><td>FM Rate (number of complete frequency modulations per time unit), FM Extent (mean peak-to-peak variation of each frequency modulation), FM Var (cumulative frequency variation per time unit)</td><td>+</td><td>+</td></tr><tr><td colspan="6"><strong>Tonality/Noise</strong></td></tr><tr><td>Harmonicity</td><td></td><td></td><td>Degree of acoustic periodicity, also called harmonic-to-noise ratio (higher values indicate more tonal calls)</td><td>+</td><td>-</td></tr><tr><td>Wiener Entropy</td><td></td><td></td><td>Spectral flatness of a sound, calculated as the ratio of a power spectrum's geometric mean to its arithmetic mean measured on a logarithmic scale (higher values indicate more noisy calls)</td><td>+</td><td>-</td></tr><tr><td colspan="6"><em>In the amplitude domain</em></td></tr><tr><td>AM</td><td>s-1, dB/ s or dB</td><td></td><td>AM Rate (number of complete cycles of amplitude modulation per time unit), AM Extent (mean peak-to-peak variation of each amplitude modulation), AM Var (cumulative amplitude variation per time unit)</td><td>+</td><td>-</td></tr><tr><td>Amp</td><td>Amplitude</td><td>dB</td><td>Level of energy, loudness of the vocalization</td><td></td><td></td></tr><tr><td colspan="6">dB: Decibel, Hz: Hertz, s: seconds</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Descriptors of vocal behaviour (A) and vocalization structure (B-E) in time and frequency domains.</figcaption></figure>

vocalizations and on each vocalization) may be automatically obtained if the signal to background noise ratio has been optimized during data collection. Using an automatic amplitude threshold, detection of start and end of each vocalization allows to measure the vocalization duration and descriptors of vocal sequences by only taking into account a percentage of energy contained in the vocalization. Automatic detection of rather stereotypical sounds can also be achieved using a template-based detection, which is an approach involving creating templates or reference patterns that represent the characteristics of the target calls, and comparing these templates to the audio data to detect instances of

the calls (Araya-Salas et al., 2023; Nolasco et al., 2023). Both methods avoid repetitive manual measures on oscillograms or spectrograms, and thus increase the reliability of the analysis (Fig. 1).

While some indicators can be obtained rather directly based on the waveform, frequency-related parameters require a spectral analysis involving computing a Fast Fourier Transform (FFT; Table 1). This mathematical algorithm decomposes a complex signal into its individual frequency components, thus transforming time-domain signals into their frequency-domain representation. This allows for the extraction of important acoustic features including characteristics of the quartiles,
formants, and fundamental frequency, most often using an acoustic analysis software such as Raven (K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology, 2023), Praat (Boersma and Weenink, 2001) or specific packages on R [e.g., warbleR (Araya-Salas and Smith-Vidaurre, 2017), Seewave (Sueur et al., 2008), Sound-Chunk (Villain and Renaud-Goud, 2023) and Soundgen (Anikin, 2019)], or Python (Lapp et al., 2023). FFT computing requires choosing a sliding window for the computation of the mean spectrum of the vocalization. The size of this window (in number of points or in seconds) will affect the spectrogram image (Fig. 1D) and on the precision of the measures on a mean spectrum (Fig. 1E), and hence needs to be adapted to the targeted vocalization and reported.

Once the vocal parameters have been extracted, they can either be analyzed individually using statistical models, or computed together to determine patterns across treatments or individuals and/or classify the calls using e.g. principal component analyses, cluster analyses, or canonical discriminant analyses. Yet, with potentially large datasets, unknown linear relations across data and a potential lack of conformity to the assumptions of many classical statistical models, machine learning algorithms offer a versatile alternative to more classical statistical analyses, and have become more common than ever in bioacoustic analyses (Rutz et al., 2023; Valletta et al., 2017).

### 2.4.2. Machine learning analyses

Machine learning refers to the development of algorithms and models that learn from patterns in the data and generate predictions on previously unseen data (Valletta et al., 2017). This computing encompasses different types of models, including supervised and unsupervised learning. Supervised learning, used in e.g. decision trees, support vector machines, random forest computations, or convolutional neural networks, involves training a model using labeled examples of vocalizations, where each vocalization is associated with a predefined category or label. The model learns from these labeled examples to make predictions or classify new, unseen vocalizations, based on the patterns and features learned during training. Unsupervised learning, on the other hand, involves training a model without explicit labels or categories. The model explores the data and identifies patterns, structures, or similarities between vocalizations on its own, achieving tasks like clustering similar vocalizations together or discovering unknown patterns within the data. Unsupervised learning covers a large range of analyses, from dimensionality reduction and feature extraction (performed e.g. in principal component analyses), to clustering (e.g. K-means) and Gaussian mixture model. For further details on these models and the use of machine learning in acoustic analyses, see Bianco et al. (2019) and Vranken et al. (2023).

# 3. Vocal features as indicators of welfare components

## 3.1. Methodology of literature collection and organization

This section provides an overview of the use of acoustic features to detect and quantify various aspects of livestock welfare. It does not constitute a systematic review, but rather aims at providing readers with a summary of the current state of the field. The literature included in this section was collected using Google Scholar and Web of Science search engines, using the following key words: bioacoustics; vocalizations; calls; husbandry animal names in English and Latin; sound detection; sound classification. Focus was given to articles published after 2010, with exceptions for studies illustrating an aspect of vocalization analysis that has not been recently investigated.

The review is structured by type of indicator investigated: 'static information' inherent to the attributes of the animal (physical characteristics and individuality) and 'dynamic information', which relates to the state of the animal at a particular instant (health, physiological state, behaviour and affect; e.g. Aubin and Mathevon, 2020). These aspects were given focus considering their importance in different elements of

welfare, such as the adaptation to the environment and health, affective states and feelings, and long-term quality of life, as recently described by Reimert et al. (2023), though the relevance of specific parameters may differ across farming systems. Within each aspect, different elements, e. g. infections, reproductive status, or age, are discussed, with examples from one or more species. The different aspects covered in the review are summarised in Table 2.'

Importantly, the review aims to differentiate between studies focused on 1) exploring acoustic indicators of a specific condition or context, in which the authors tested if a condition (e.g. the presence of a disease) or context (e.g. display of a specific behaviour) affects the acoustic output in a certain species, and 2) producing and potentially also validating an algorithm that can detect that specific condition or context among various individuals. The outcome of the studies (empirical knowledge, algorithm, validation of an algorithm) will be explained as an illustration of how close a specific condition or context is to be recorded automatically in practice.

## 3.2. Static information

### 3.2.1. Physical characteristics

Animal physical characteristics, including the weight and developmental stage, influence the structure and size of the vocal organs and vocal production, hence impacting the resulting acoustics features. Acoustic parameters may therefore allow the detection of physical attributes, including sex, age, or body size and weight, which are indicators that commonly need to be assessed by farmers when they are not straightforward (e.g. pig weight, chick sex). This remote detection using vocal indicators would prevent the need for stressful, repetitive manipulation of animals, improving their welfare while reducing the need for skilled workers' time.

Sex identification using vocal parameters has become increasingly popular, especially in poultry. Though usually not detectable to the untrained ear, the sex of one-day-old chicks has been shown to affect $f_0$ (Guo et al., 2014), the frequency of the second formant (Pereira et al., 2015), as well as other parameters in the time and frequency domains (Sadeghi and Banakar, 2017). More recently, Cuan et al. (2022) developed a deep learning algorithm capable of classifying the sex of 240 chicks with an accuracy close to 90%, showing great potential for automatic detection on a large scale. It is however worth noting that, in this study, the chicks were individually placed in a box to collect vocalizations, which still required time, manning, and presumably created some level of stress for the animals. Besides chicks, a difference in $f_0$ and other acoustics parameters has also notably been revealed between males and females in piglets (Cordeiro et al., 2018), and goat kids (Briefer and McElligott, 2011). Interestingly, the same sex differences were not detected in the calls of calves (Padilla de la Torre et al., 2015). However, so far, we are not aware of any study that developed an algorithm capable of automatically classifying the sex of these species.

Remote detection of an individual's age can also be of interest, for instance in the case of extensive production where the exact moment of birth may not be accurately controlled. The frequency of chick calls has been shown to be inversely proportional to their age (Fontana et al., 2015), while markers of calf age, such as formant frequencies, were detected in free ranging individuals (Padilla de la Torre et al., 2015). Similarly, $f_0$ and the frequency of the first formant were defined as reliable acoustic parameters to estimate piglet age (Cordeiro et al., 2018). Importantly, while these studies highlight that vocalizations could be used as a tool to determine the age of most husbandry animals, sensor-based methods to automatically detect age are yet to be created for these species. Age-related changes in vocal parameters were also recorded in goat kids (Briefer and McElligott, 2011). Using a form of supervised learning, Favaro et al. (2014) developed a model that could classify the contact calls of goat kid according to their age, with over 90% accuracy. Yet, although promising, this study included ten animals, and only one female, and therefore warrants to be replicated on a
<table><caption>**Table 2**
Summary of welfare aspects investigated in the review by species.</caption><thead><tr><th>Type of welfare indicator</th><th>Pig</th><th>Poultry</th><th>Cattle</th><th>Goat</th><th>Sheep</th></tr></thead><tbody><tr><td><strong>Static information</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><em>Physical characteristics</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sex</td><td>Cordeiro et al. (2018)</td><td>Guo et al. (2014)<br>Pereira et al. (2015)<br>Sadeghi and Banakar (2017)<br>Cuan et al. (2022)</td><td></td><td>Briefer and McElligott, (2011)</td><td></td></tr><tr><td>Age</td><td>Cordeiro et al. (2018)</td><td>Fontana et al. (2015)</td><td>Padilla de la Torre et al. (2015)</td><td>Briefer and McElligott, (2011)<br>Favaro et al. (2014)</td><td></td></tr><tr><td>Body weight</td><td>Garcia et al. (2016)</td><td>Fontana et al. (2017)<br>Herborn et al. (2020)</td><td></td><td>Briefer and McElligott, (2011)</td><td></td></tr><tr><td>Individuality</td><td>Blackshaw et al. (1996)</td><td></td><td>Green et al. (2019)<br>Padilla de la Torre et al. (2015)<br>Yajuvendra et al. (2013)</td><td>Favaro et al. (2014)</td><td></td></tr><tr><td>Group membership</td><td></td><td></td><td></td><td>Favaro et al. (2014)</td><td></td></tr><tr><td><strong>Dynamic information</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><em>Health</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Couging</td><td>Vandermeulen et al.<br>(2013)<br>Guarino et al. (2008)<br>Exadaktylos et al.<br>(2008)<br>Silva et al. (2008)</td><td></td><td>Vandermeulen et al. (2016)<br>Carpentier et al. (2018)</td><td></td><td></td></tr><tr><td>Respiratory diseases</td><td>Lee et al. (2015)<br>Hong et al. (2020)</td><td>Whitaker et al. (2014)<br>Sadeghi et al. (2015)<br>Banakar et al. (2016)<br>Huang et al. (2019)<br>Cuan et al. (2020)<br>Carpentier et al. (2019)(Liu et al., 2020)</td><td></td><td></td><td></td></tr><tr><td>Heat stress</td><td>Ferrari et al. (2013)<br>Hillmann et al. (2004)</td><td>Moura et al. (2008)<br>Liu et al. (2018)<br>Lee et al. (2015)</td><td></td><td></td><td></td></tr><tr><td>Physiology</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Oestrus</td><td></td><td></td><td>Chung et al. (2013)<br>Devi et al. (2019)<br>Peng et al. (2023)</td><td></td><td></td></tr><tr><td><em>Behaviour</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Feeding behaviour</td><td></td><td>Huang et al. (2021)<br>Aydin et al. (2014)<br>Aydin and Berckmans (2016)<br>Du et al. (2018)</td><td>Clapham et al. (2011)<br>Navon et al. (2013)<br>Peng et al. (2023)</td><td>Navon et al. (2013)</td><td>Navon et al.<br>(2013)<br>Galli et al.<br>(2011)</td></tr><tr><td>Unwanted behaviours</td><td>Heseker et al. (2024)</td><td>Bright (2008)</td><td></td><td></td><td></td></tr><tr><td><em>Affective states</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Stress</td><td>Leliveld et al. (2017)<br>Schön et al. (2004)<br>Vandermeulen et al.<br>(2015)<br>Chapel et al. (2018)<br>Tallet et al. (2013)<br>(Briefer et al., 2022a)</td><td>Jakovljevic et al. (2019)<br>Herborn et al. (2020)</td><td>Schnaider et al. (2022)</td><td></td><td></td></tr><tr><td>Pain</td><td>Da Silva et al. (2019)<br>Marx et al. (2003)<br>Weary et al. (1998)<br>Schön et al. (2009)</td><td>(Machin, 2005)</td><td>Watts and Stookey (1999)</td><td>Wagmann et al. (2018)</td><td></td></tr><tr><td>Frustration / Fear</td><td>Schön et al. (2009)</td><td>McGrath et al. (2017)<br>Zimmerman et al. (2000)<br>Lee et al. (2015)</td><td></td><td></td><td></td></tr><tr><td>Positive states</td><td>Briefer et al. (2019)<br>Briefer et al. (2022a)<br>Villain et al. (2020)</td><td>McGrath et al. (2017)</td><td></td><td></td><td></td></tr></tbody></table>

larger scale.

Automatic and remote monitoring of body weight, and associated growth patterns, has the potential to facilitate the work of farmers for various type of husbandry production, providing real-time information about the status of the flock and removing the need for stressful and time-consuming weighing sessions. After finding a negative correlation

between body weight and the frequency of calling in chicks (Fontana et al., 2015), Fontana et al. (2017) developed a model to predict the weight of a flock of chicks housed in a commercial broiler farm, based on the peak frequency in their vocalization, which showed promising results to monitor large groups of broilers. Herborn et al. (2020) additionally showed that the spectral entropy of early-life distress calls in
chickens was a predictor of both immediate and future low weight gain and mortality, highlighting this indicator as a promising way to monitor chick welfare. In pigs, although a study showed that grunt formant frequency could be a relevant indicator of body size (Garcia et al., 2016), the use of acoustic signals to measure weight remains understudied. Similarly, while the body size of goat kids were shown to negatively correlate to most frequency parameters (Briefer and McElligott, 2011), a predictive model is yet to be developed.

### 3.2.2. Individuality

It is noteworthy that individual identity can be detected in the vocalizations of many species, potentially allowing a more individual monitoring and tailored care of the animals. Models based on the analysis of spectral and temporal acoustic features, including $f_0$, formants, jitter and shimmer, have been able to classify vocalizations according to the individual who produced them in goat kids (Favaro et al., 2014), ewes and lambs (Laliotis et al., 2023), as well as cows and calves (Green et al., 2019; Padilla de la Torre et al., 2015; Yajuvendra et al., 2013). Although vocal individuality has not been investigated in pigs, early research has similarly showed evidence of individual markers in the vocalizations of sows during nursing (Blackshaw et al., 1996). The individuality of animal vocalizations implies that, although vocal monitoring of livestock will likely be applied at herd level in practice, information regarding health, welfare or behaviour could potentially be additionally mapped at an individual level, allowing to identify e.g. sick animals within a group. Nevertheless, although an unsupervised model could in theory distinguish among different individuals, the model would still need to be taught which vocalizations belong to which specific individual. Each animal would therefore need to be isolated and their vocalizations recorded - a method that remains theoretical and, for now, unrealistically applicable in a farm setting.

Bioacoustics analyzes may have the potential to inform about the structure of a herd, in species in which vocalizations are affected by the social environment. Favaro et al. (2014) used artificial neural networks to classify the calls of goat kids according to their group membership, with an accuracy close to 80 %. It is noticeable, though, that the goat kids included in the study only came from three different groups.

## 3.3. Dynamic information

### 3.3.1. Health

Because respiratory diseases, infections, and other health related disorders impact the functioning of the respiratory system, including the capacity of the lungs, congestion in the trachea and larynx, and salivation in the oral cavity, their incidence may be detected in the vocalizations of animals. This detection could prevent the need for likely stressful physical evaluation of the animals, while allowing for a more targeted and potentially earlier start of intervention.

Coughing, one of the clinical symptoms of bovine respiratory disease (BRD), has been automatically recorded in groups of calves, on farm, with use of algorithms based on supervised learning (Carpentier et al., 2018; Vandermeulen et al., 2016). Vandermeulen et al. (2016) showed that, in 80 % of the cases, an increase in coughing, detectable via the algorithm, coincided with the development of BRD, indicating that this type of monitoring has the potential to identify early onset of BRD in calves. Nevertheless, while the detection was promising, with a precision close to 100 % in some batches, other repetitions showed a relatively high amount of false positives, reducing the precision below 60 %. The reliability of the detection thus deserves further improvement.

Several studies have focused on developing an automatic detection of clinical coughing in pigs, at barn level, following the results of early studies indicating acoustic differences between cough produced by pigs with lung infection as compared to cough provoked by inhalation of citric acid in healthy pigs (Ferrari et al., 2008). Already in 2008, Exadaktylos et al. developed an algorithm for real-time detection of sick cough sounds in groups of pigs, based on the frequency of their calls,

with 82 % of correct identification in a laboratory setting (Exadaktylos et al., 2008). A follow-up study, investigating the same patterns in field conditions, reached 85 % accuracy in the detection of cough sounds (Guarino et al., 2008). The same year, Silva et al. (2008) created an algorithm to detect repeated coughing and localize it within the barn (based on the time arrival difference among different microphones), with the aim to identify hazard zones and limit the medical interventions to single pens rather than the whole barn (Silva et al., 2008). Five years later, the SoundTalks pig cough monitoring system was commercially launched, and has since been deployed in over 20 countries (Boehringer Ingelheim Vetmedica GmbH, 2022; Vandermeulen et al., 2013).

Other diseases may be recorded in pig vocalizations; Lee et al. (2015) developed a model to detect postweaning multisystemic wasting syndrome (PMWS), also called pig wasting disease, using supervised learning. More recently, Hong et al. (2020) developed a ‘sound-based pig anomaly detection system’ capable of automatically detecting pig vocalization and classifying them as normal grunts, healthy cough, sign of mycoplasma hyopneumoniae (MH), sign of porcine reproductive and respiratory syndrome (PRRS), or sign of PMWS. The algorithm showed good detection performance (as measured with a performance score close to 0.95) on small-scale farms, even in noisy barn environments, and could be used in real-time, showing great potential for future use on farm.

As reviewed by Li et al. (2020), different types of respiratory diseases can also be detected in the acoustic signals of poultry. Using an unsupervised method of machine learning based on gurgling noises, Whitaker et al. (2014) classified audio recordings as produced by healthy chicken or chicken infected with infectious bronchitis, with an accuracy close to 98 %. It can be noted, though, that the set up involved recordings of groups of only six chickens placed in isolation chambers. Similarly, Sadeghi et al. (2015) developed a model to record infections with Clostridium perfringens, by comparing vocalizations of healthy and infected chickens placed in two separate cages. Banakar et al. (2016) developed a supervised model to automatically record the incidence of Newcastle disease, bronchitis virus, and avian influenza in an experimental set-up involving 240 chickens, based on the extraction of 100 statistical features from the time and frequency domains. Additionally, Huang et al. (2019) and Cuan et al. (2020) published methods to automatically detect the presence of avian influenza based on mel-frequency cepstral coefficients and other vocal features of chicken's vocalizations. While both studies reported a relatively good detection accuracy for their models (averaging 90 %), the development of the algorithms was however only based on vocalizations from few chickens [fourteen in Huang et al. (2019), while the number of experimental animals was not mentioned in Cuan et al. (2020)], and the same chickens were used to record the calls of healthy vs. later infected animals. Sneezing, a clinical sign of many respiratory diseases, has also been recorded automatically in broilers in a similar experimental set-up involving 51 chickens housed in two cages (Carpentier et al., 2019). Although promising, it is important to note that these types of experimental studies remain only a first step towards automatic monitoring of poultry health in farms. Further studies are required to test and refine these algorithms in practice, i.e. in larger flocks with background noise and potential confounding factors. A good example of such study would be Liu et al. (2020), who developed a method to automatically detect broiler abnormal sounds, including coughing and snoring, in a broiler house containing 20 000 animals, with an accuracy exceeding 93 %.

Besides infection and other respiratory disorders, certain physical or environmental conditions influencing the tension, respiration, and salivation of individuals may also affect the acoustic features of their calls. Ferrari et al. (2013) showed, for instance, a clear difference between the calls of piglets subjected to heat stress as compared to the calls of their peers housed under conventional temperatures. Reversely, Hillmann et al. (2004) found that the use of high-frequency vocalizations is related to behavioural thermoregulation in low temperatures, in medium to heavy weighted pigs. It is unclear, though, whether the
changes in vocal parameters observed in that study were directly due to the reduction in temperature or to the huddling behaviour that resulted from it. Heat stress has also been investigated in poultry. Moura et al. (2008) reported a correlation between the thermal comfort of chicks and the amplitude and frequency of the calls produced by the reared group, both in experimental and practical settings. In an equivalent study, Liu et al. (2018) reported a correlation between the room temperature and the number and onset of vocalization produced by turkeys kept in experimental conditions. Lee et al. (2015) developed a stress detection system based on the vocalizations of laying hens housed in commercial battery type cages. The algorithm, based on supervised learning, showed great promise in detecting and categorising stress calls, including physical stress triggered by an increase in temperature.

While other conditions may be recorded using bioacoustics analysis, it is worth noting that not all health parameters may be detectable in animal vocalizations. For instance, Risi et al. (2008) for instance failed to detect a clear difference in the screams of piglets with or without traumatic arthritis.

### 3.3.2. Physiology

Animal sounds may be used to detect specific elements of their physiological state in a remote and real-time fashion. Vocal features of pigs have long been correlated with endocrine stress responses, which, in turn, can inform about general stress states and hence welfare. Already in 1998, Schrader and Todt (1998) showed that increasing rates of squeal-grunts correlate with an increase in plasma adrenaline, while an increase in cortisol correlates with a reduction in the iteration of grunts. Vocalizations of (mini)pigs have more recently been shown to be related to an activation of motor and pre-motor cortical regions of the brain (Palma et al., 2022), confirming a strong relationship between vocal features and neuroendocrine physiology.

Reproductive hormones produced during oestrus may elicit hormone-dependant morphological changes in the vocal organs, including the vocal cords and larynx, resulting in distinctive auditory features (Devi et al., 2019). Monitoring these changes could enable farmers to detect this state without having to manipulate the animals, hence reducing stress. Chung et al. (2013) developed an algorithm to detect oestrus in Korean native cows housed in a commercial loose barn. The calls of cows in oestrus (as determined by visual observations including mucous vaginal discharge, restlessness, standing heat, and acceptance of mounting) were recorded and used to train a model in a supervised approach. Using the mel-frequency cepstrum coefficients, the model was able to discriminate between calls of cows in oestrus and calls from non-oestrus cows in the same group with 94 % accuracy. Although promising, application of such method in a wider range of husbandry setting and in real-time warrants further study. Devi et al. (2019) additionally reported significant differences in acoustic features among the calls produced by water buffaloes during the four phases of oestrus (i.e., proestrus, estrus, metestrus and diestrus). Cut-off points in terms of call duration, intensity, formants, $f_0$, and noise/harmonic ratios were found to enable a distinction between these phases, paving the way for the development of a method for automatic detection of early signs of oestrus (Devi et al., 2019). In this study, however, the calls were not compared to vocalizations produced by animals outside of the oestrus cycle. The distinctiveness of e.g. proestrus calls as compared to other types of calls remains therefore to be investigated. Recently, Peng et al. (2023) developed a model to record oestrus calls among other types of calls in Japanese Black cattle and reported some limitations in the automatic detection due to the similarity in acoustic features among oestrus calls and e.g. feeding or contact calls under noisy conditions. For now, acoustic monitoring appears as a valuable complement to conventional methods of oestrus detection, rather than a stand-alone tool.

### 3.3.3. Behaviour

Because certain behaviours may be related to particular vocalizations in farm animals, or be associated with the production of distinctive

non-vocal sounds (e.g. chewing, locomotion), bioacoustics tools may be used to detect the performance of behaviours that are of interest for the monitoring the welfare of farm animals, such as feeding and drinking or maladaptive behaviours.

Monitoring feeding behaviour may be crucial to ensure the health and welfare of farm animals kept under specific husbandry conditions, such as outdoor production. In grazing ruminants, ingestive behaviour has been recorded based on biting and chewing (Clapham et al., 2011; Galli et al., 2006; Galli et al., 2011; Ungar and Rutter, 2006). Clapham et al. (2011) automatically recorded the ingestive behaviour of grazing cows using a microphone fixed on the side of the head of the animal. In this specific study, an audio analysis software was used to detect and classify biting and chewing sounds with a 95 % accuracy, with only little disturbance from adjacent livestock and ambient noise (Clapham et al., 2011). A later study confirmed that jaw movement can be accurately detected and identified using acoustic monitoring in cattle, sheep, and goats grazing in pasture, if intense ambient noises can be avoided (Navon et al., 2013). Applying a similar method, Galli et al. (2011) showed that an accurate estimate of individual dry matter intake can be obtained using acoustic monitoring. The detection was based on the chewing energy per bite and amount of energy recorded in chewing sounds of grazing sheep, and turned out to be relatively effective regardless of the type of forage provided or structure of the grass. Importantly, however, all the studies previously mentioned recorded individual feeding behaviour by placing microphones on the head of the animal - an approach that would be difficult in a farm setting. A practical solution may be to integrate a microphone in the collar of ruminants, as developed in Schirmann et al. (2009) to monitor rumination, or in the ear tag of individually identified livestock.

Feeding behaviour has also been investigated in Japanese Black cattle kept indoors, based on the performance of so-called feeding calls, i.e. short and loud calls produced by fattening cattle prior to feeding (Peng et al., 2023). In this study, the microphone was placed in the barn, and vocalizations were recorded at group level. Although the algorithm developed showed promising results to detect feeding calls, the authors highlighted that similarities in terms of amplitude and duration between this type of call and other vocalizations, including contact calls or oestrus calls, resulted in incorrect classifications and therefore reduced the accuracy of detection (Peng et al., 2023). The authors also suggested that future studies could opt for individual recordings of vocalization, e. g. by fixing a microphone near the mouth – as previously done in grazing ruminants. It is important to note, though, that the feeding calls recorded in the study were also referred to as ‘hunger’ calls. Unlike chewing or biting sounds, it remains unclear whether the performance of such calls, potentially indicative of feed motivation, can be directly correlated to feed intake. This calls for attention towards the selection of the vocal indicators recorded and careful interpretation in terms of livestock health and behaviour.

Monitoring feeding behaviour may also be of interest to control the feed intake of large flocks of animals. Recently, Huang et al. (2021) developed a method to detect poultry eating behaviour based on the specific vocalizations produced by chicken while eating, in an experimental set-up involving a group of 18 chickens. Adopting another approach, Aydin et al. (2014) developed an algorithm to automatically record pecking sounds in a group of 12 broiler chickens, with a 93 % correct identification. The study showed a 99 % correlation between the detected number of pecking and the feed intake of the group. In a follow-up study involving a similar set-up, automatically-recorded pecking sounds were used to determine the meal size, meal duration, number of meal per day and feeding rate, with estimates averaging 90 % accuracy (Aydin and Berckmans, 2016). These studies show great promise for such tool to automatically monitor feed intake and feeding behaviour of broilers remotely and in real-time, though the method needs validation in a husbandry setting involving a larger number of birds and environmental noise (Li et al., 2020).

Although receiving relatively less focus, vocalizations of laying hens
have also been investigated; Du et al. (2018) tested a method to monitor laying hens at night in an experimental set-up involving sound source localization. The model detected abnormal status at night for specific areas of the enclosure by monitoring the number of vocalizations and acoustic features. The model could detect an increased number of vocalization at night, in otherwise relatively silent flocks, which, as suggested by the authors, may indicate a lack of feed or water availability. Increase in pecking sounds for water intake may for example be associated with a shortage of solid feed, and potentially with stereotypical behaviour. Although promising, this method calls for follow-up studies, including investigations of the underlying causes of these 'abnormal' soundscapes, and establishment of their implication for laying hens' health and welfare.

Additionally, auditory cues could be used to detect and inform about the prevalence of unwanted, or maladaptive, behaviour within a herd, or flock, often indicative of compromised welfare. In an early study, Bright (2008) for instance found distinct call types and rate between feather pecking and non-feather pecking flocks, with the latter showing increased display of vocalization, including squawks. Recent, ongoing work also focus on recording pigs' vocalizations, including screams, as an early detector of tail biting (Heseker et al., 2024; C. Tallet, personal communication, 2023). Yet, automatic systems to monitor social behaviours using vocal cues, whether positive or negative, remain scarce. This may be explained by the fact that most studies are focusing on the monitoring of animals' affective states, including distress or fear, rather than on the detection of specific behaviours.

### 3.3.4. Affective state

Affect is a critical component of animal welfare, especially in the context of livestock farming. Assessing the emotions of animals throughout the different stages of their life and during specific husbandry practices is crucial to monitor their well-being, evaluate the impact of these practices, and potentially improve the way livestock is housed and cared for (Désiré et al., 2002). Yet, accessing such transient and complex mental states is a challenge, especially in practice. Since the emotional state of an individual can have a direct influence on the structures involved in sound production and therefore on the sound structure itself (e.g. sound duration, energy or $f_0$), vocal cues have long been suggested as promising indicators of affect (Briefer, 2012; Briefer, 2020; Villain and Briefer, 2024). The monitoring and detection of livestock emotion through bioacoustic analyses has considerably evolved in the last decades.

Stress, broadly described as the emotional responses (including physiological, behavioural and cognitive changes) induced by a stimulus posing an actual, or perceived, threat to homeostasis and general fitness [definition adapted from Moberg (2000)], is one of the most studied affective state in relation to vocalizations. Numerous studies have reported a relation between stress and the type, number, and characteristics of vocalizations emitted by livestock (e.g. Schnaider et al., 2021; Liu et al., 2018; Ferrari et al., 2013). Studies have recently intended to automatically detect general stress calls, for instance in broilers subjected to high heat or manipulation. In that study, Jakovljevic et al. (2019) showed that stress calls could be detected automatically among broiler calls with the use of a support vector machine, though the low number of controls included in the dataset warrants caution. The call rate and proportion of individual call types has also been shown to correlate to emotional reactivity in pigs, i.e. the predisposition of an individual to react in a challenging, or stressful, situation, as measured by heart rate and behavioural parameters (Leliveld et al., 2017). A method to automatically monitor and record the level of stress in pigs calls has been developed and tested across different stress-inducing situations. Through a combination of linear prediction coding and artificial neural networks, the system 'STREMODO' (stress monitor and documentation unit), can detect stress vocalization of pigs with few recognition errors (<5 %), even in noisy environments, and has been brought forward as a reliable tool to monitor pig welfare, while being

potentially applicable to other species (Schön et al., 2004). Other methods have since then been published, focusing primarily on the detection of pig screams as a measure of stress and negative welfare (Vandermeulen et al., 2015).

Although recording a general state of stress or a predisposition for stress is important, many studies have intended to monitor specific types of stress instead, displayed in events of interest. Different stressful situations, such as crushing and restraint, have been shown to lead to different characteristics of vocalizations in piglets (Chapel et al., 2018), which can be described both in terms of types of calls and acoustic features (Tallet et al., 2013). This indicates that specific events of negative valence (associated with an unpleasant or negative state) could be identified with bioacoustic tools, as suggested by other studies (Cordeiro et al., 2018; Da Silva et al., 2019). Similarly, in chickens, Herborn et al. (2020) showed that chicks in distress presenting thermoregulatory behaviour or behavioural asynchrony in the first days of life, displayed a specific call characterised by low spectral entropy. The authors therefore suggested that the spectral parameters of early-life distress calls may be used as an indicator of chicken welfare. More recently, a study by Briefer et al. (2022a) developed a method to automatically detect and classify the context of production of pig calls (isolation, castration, restraint, etc.), showing great promise for the automatic detection of specific events (e.g. crushing of piglets) in practice.

Focusing on specific negative emotions rather than situations, Da Silva et al. (2019) used a form of supervised learning model to automatically detect states of cold/heat, pain, hunger and thirst in piglets. The results showed a moderate efficacy to classify cold/heat, hunger, or thirst, but a high accuracy (93 %) to detect pain. This outcome is in line with previous studies highlighting the potential of vocalizations to monitor pain in young piglets, especially because painful situations seem to trigger the production of screams, a specific type of vocalization whose vocal parameters (high frequency and intensity) are significantly distinct from other calls types (Marx et al., 2003; Weary et al., 1998). Similarly, the type, number, and characteristics of calls have been used for decades to record acute pain in cattle (e.g. Watts and Stookey, 1999), goats (e.g. Wagmann et al., 2018), and poultry (e.g. Machin, 2005). It is however important to note that longer lasting pain, including chronic pain, has been suggested to lead to subtle differences in the production of normal vocalizations, rather than to a specific type of vocalization (Manteuffel et al., 2004). McGrath et al. (2017) and Zimmerman et al. (2000) suggested that hens produce very specific calls of long duration and high intensity called 'whines' and 'gakel calls' when frustrated, i.e. when feed is anticipated but not delivered. In addition, Lee et al. (2015) developed an algorithm capable of detecting fear-evoked vocalizations of laying hens housed in battery cages with an accuracy above 93 %. It can be noted, though, that the presumed state of fear was evoked by hitting the cage with a stick. With no other measures reported to validate the state of fear, it cannot be excluded that the birds were experiencing auditory pain or discomfort, rather than – or in addition to – fear per se. In a study from 2009, (Spinka et al., 2009) reported that vocal parameters of piglets were strongly correlated to aggression prior to feeding, but that vocalization did not appear as a reliable indicator of hunger. Altogether, these results suggest that affective states of high arousal (i.e., intense bodily activation) such as acute pain, fear or frustration, may lead to more specific, and therefore more easily detectable, sounds compared to relatively milder states such as hunger, thirst or discomfort, although further studies are needed to confirm this hypothesis. While a comparable high bodily response and interplay of stress could in theory render difficult the distinction among these high arousal states, studies suggest that subtle differences in vocal parameters, notably in terms of shifts of main energy and altered degrees of noise in the frequency-spectrum, could allow the distinction between pain, fear, frustration, or competition for feed (Schön et al., 2009).

Despite a notably larger focus on the detection of affective states of negative valence, the potential of vocalizations to indicate positive
emotions has also been studied, and effects of the valence of the affective state of individuals (whether the animal is experiencing a pleasant or positive state vs. an unpleasant or negative state) on their vocal characteristics has been demonstrated, as reviewed by Briefer (2012), (2020) and Laurijs et al. (2021). For instance, it is known that positive situations lead to vocalizations of lower frequency and duration in pigs, at the exception of grunts, whose frequency increases between negative and positive states (Briefer et al., 2019; Briefer et al., 2022a). In horses, specific types of calls such as nickers, short whinnies and snorts, seem to be mostly observed during positively-valenced situations (reviewed in Laurijs et al., 2021). Specific emotions have also been shown to be indicated by distinct call types. Anticipation in hens has been linked to the utterance of fast clucks and food calls, which are vocalizations of short duration produced in succession (McGrath et al., 2017). In pigs, anticipation of social reunion was associated with short and higher pitched grunts, compared to frustration of not being reunited as expected (Villain et al., 2020). More recently, a study using over 7400 calls from more than 400 pigs showed that the valence can be accurately automatically classified based on pig calls (Briefer et al., 2022a). Although further research is needed to develop tools usable on farm by farmers and veterinarians, this type of research is promising for the future assessment of affect in practice.

It is worth noticing, though, that bioacoustics studies in relation to assessing affect seem far more advanced in pigs, a species that is highly vocal across situations, compared to other domestic species. While Schnaider et al. (2022) recently showed significant differences in the vocal expressions of cow and calves separated (a negatively valenced event) vs. reunited (a positive event), the automatic monitoring of affect in cattle remains at an early stage. Overall, clear evidence of a relationship between positive emotions and vocalizations still needs to be established in most species, including cattle, sheep and chicken (Laurijs et al., 2021), and research on the use of vocal cues to detect and monitor emotions in an applied and automated manner in practice remains lacking.

## 4. Discussion

This review presented a wide range of studies using bioacoustic analyses to record elements necessary to assess the welfare of livestock animals, at various degrees of automatization. Although this collection of research illustrates the potential of bioacoustics to become a common tool to monitor livestock in a remote, non-invasive and hence welfare friendly way, it also highlights the disparity in methodology used, and the general lack of practical applications of the methods developed in an academic setting. In fact, despite the potential use of bioacoustics to record a wide array of physiological, health and behavioural parameters, so far, only one specific field of research, the monitoring of pig coughing, led to the development of a product currently available for farmers. This observation may be explained by a combination of reasons, developed hereafter.

Based on the literature reviewed here, we propose a scheme to classify the status of development of a bioacoustic tool, from the detection of the acoustic marker of a certain condition, to the production of an available device (commercially or more ideally open source) to record this condition (Fig. 2). Importantly, these steps are not necessarily followed. Advances in machine learning-based algorithms have the potential to enable i.e. skipping of phase 1 or acceleration of phase 1-3. Nevertheless, a fully 'AI-driven' approach (such as 'black box' models in which the explanation for the classification is unknown) likely reduces the biological interpretability of the resulting classification/ distinction. Depending on the specific condition under study, a balance is therefore to be found between a gradual approach (starting from phase 1) and a more direct approach to phase 2 or 3 using e.g., black box models.

Most studies reporting the development of a method to monitor a certain parameter or behaviour are currently at phase 2 (Classification of a condition in the sounds; Fig. 2). They are presenting methods to classify calls as being produced by e.g. a group of animals presenting a certain disease vs. a control group, or to discriminate among e.g. feeding vs. contact calls. The models developed for these studies are often promising, presenting a relatively high percentage of accuracy. Yet, it is important to keep in mind the range of call types and sample size used to

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Proposed scheme to classify the status of development of a bioacoustics tool to automatically record a physiological, behavioural, or affective state.</figcaption></figure>
develop these methods. Algorithms developed using vocalizations from
a handful of animals or using the same calls to train and test the models
may easily result in artificially high model robustness. While these
studies are a necessary and valuable addition to the field as proofs of
concept, the models they are developing may not be practically appli-
cable. For instance, Chung et al. (2013) developed an algorithm that
could automatically detect calls of cattle in oestrus as compared to
‘normal’ calls of the herd with over 90 % accuracy. Yet, when Peng et al.
(2023) later intended to detect oestrus among a wider variety of calls
including hunger calls, contact calls, and calls performed in noisy con-
ditions, the accuracy of detection was reduced, notably because these
calls shared similar characteristics in terms of amplitude and duration.
This example illustrates the complexity of advancing from phase 2 to
phase 3 (Fig. 2).

Another potential reason for the difficult transition from phase 2 to
phase 3 is the relative lack of replicability and sharing of the methods. As
seen in this review, authors use a variety of complex analysis methods
and approaches to develop bioacoustics detection. While the general
method of analysis is usually explained in details, many aspects of the
analysis, from specifics of the coding to the size of the FFT trans-
formation window, often remain unavailable. Reversely, in articles with
a strong technical focus, it is sometimes complex for the reader to crit-
ically evaluate the capacities of a model or a methodology when crucial
elements, like the number of animals included in the data sampling, are
left out. As a result, rather than intending to reproduce and refine
published methods, many authors seem to prioritize creating their own
methodology. In addition, this lack of methodological standard extends
to the validation process. The criteria used to evaluate the accuracy or
sensitivity of the models often differ between studies, while the bio-
logical validation, i.e. quantifying to what extend the algorithm actually
records the condition or emotion it is intended to, is almost never
addressed, and there is no strict rules for standardization nor replica-
bility. This lack of validation is a particular concern in studies using a
black box approach, starting directly at phase 2 or 3 without considering
phase 1. This observation highlights the critical need to close the gap
between acoustic engineers and ethologists, as further addressed later.
The relative lack of methodological consistency across bioacoustics
studies can be also linked to the lack of open databases. As previously
suggested by McLaughlin et al. (2019), the creation of such publicly
available tool would significantly improve the study of animal sounds
for welfare assessment, providing enough variety of inputs to develop
robust, deep-learning based algorithm. Yet, although databases exist for
wild species (the Macaulay Library at the Cornell Lab of Ornithology
(2023) gathers for instance almost 2 million calls from over 10000
species of bird and 2500 species of amphibians, fish and mammals, (The
Cornell Lab of Ornithology, 2023)), this is not the case for domestic
animals, with the recent exception of the Soundwel database, made of a
high number of labelled pig calls known to be associated with negative
or positive emotional valence (Briefer et al., 2022b).

Lastly, it is important to keep in mind the biological value of acoustic
analyses. As illustrated in the present study, a large focus is currently
given to the development of methods to record sound, but it is important
to ensure that our interpretation of such sounds is correct, or in other
terms, that we are recording what we think we are recording. To that
extend, it is crucial to further understand how animals communicate and
how honest these signals are, a domain that remains, so far, barely
explored in domestic species. As a result of biology and survival fitness,
certain species of animals may try to hide certain states, such as sickness
or pain, as reviewed by Carbone (2020). Importantly, the author states
that differences in pain ‘readability’ among various species are reported
more anecdotally than empirically, and points out the importance of the
individual’s relation with the human observer. Indeed, "audience ef-
fects", i.e. when the presence of individuals, or of particular individuals
of importance, around the producer, modifies the occurrence or specific
features of vocalizations produced, have been shown in several species,
including chickens (Karakashian et al., 1988). These audience effects

mean that animals in pain or experiencing a certain emotion might not
vocalize if no conspecific or human is present. Using some of the
methods developed in the article to evaluate, for example, the affective
state of isolated individuals could therefore result in wrongful conclu-
sions. How these aspects have evolved in husbandry remain mostly
unknown. The evolution of the vocal range, vocal behaviour, and gen-
eral communication patterns of animals from wild environment to
domesticated environments deserves further attention. Indeed, as
domestication alters the behaviour of animals (Jensen, 2014), we cannot
exclude that farm animals have adapted their communication to the
large number of conspecifics present around them, the noise in the barn,
or the fact that humans are caring for them. Finally, the same vocal
parameters (e.g. f<sub>o</sub>, formant, etc.), are often affected by various factors
simultaneously (e.g. body size, age, health). Automated systems are thus
likely to work more efficiently within groups of homogenous animals,
such as similar body size, age and sex, or when tracking within indi-
vidual changes. Overall, understanding animal communication, and
more precisely farm animal communication, deserves more focus and to
be investigated along with acoustic-based precision livestock farming, to
ensure a valid use of these tools for monitoring and improving animal
welfare in a wide range of settings.

In conclusion, the collection of studies presented in this review
highlights the potential for bioacoustic tools to measure different com-
ponents of animal welfare, and to improve both animal welfare and farm
management through the implementation of non-invasive, remote, and
continuous monitoring of the animals. While this field continues
growing, we highlight the need to further standardize methodological
approaches and conduct further studies on the vocal behavior of farm
animals, to ensure a successful implementation of these tools in practice.

Supplementary materials

Supplementary Code 1. R script used to produce and analysis Fig. 1.

Supplementary Sound 1. Goat vocalization used as source for Fig. 1.

CRediT authorship contribution statement

**Mathilde Coutant:** Writing – review & editing, Writing – original draft, Visualization, Project administration, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Avelyne S Villain:** Writing – review & editing, Visualization, Methodology, Conceptualization. **Elodie F Briefer:** Writing – review & editing, Visualization, Methodology, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Acknowledgements

The review was funded by the Knowledge Center for Animal Welfare
(Videncenter for Dyrevelfærd; ViD), based on a collaboration between
the Danish Veterinary and Food Administration (DVFA) and Aarhus
University.

**Appendix A. Supporting information**

Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.applanim.2024.106286.
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