Linear prediction coding analysis and self-organizing feature map as tools to classify stress calls of domestic pigs (Sus scrofa)

Utilizing vocalizations to gain insight into the affective states of non-human mammals

This review discusses how welfare scientists can examine vocalizations to gain insight into the affective states of individual animals. In recent years, researchers working in professionally managed settings have recognized the value of monitoring the types, rates, and acoustic structures of calls, which may reflect various aspects of welfare. Fortunately, recent technological advances in the field of bioacoustics allow for vocal activity to be recorded with microphones, hydrophones, and animal-attached devices (e.g., collars), as well as automated call recognition. We consider how vocal behavior can be used as an indicator of affective state, with particular interest in the valence of emotions. While most studies have investigated vocal activity produced in negative contexts (e.g., experiencing pain, social isolation, environmental disturbances), we highlight vocalizations that express positive affective states. For instance, some species produce vocalizations while foraging, playing, engaging in grooming, or interacting affiliatively with conspecifics. This review provides an overview of the evidence that exists for the construct validity of vocal indicators of affective state in non-human mammals. Furthermore, we discuss non-invasive methods that can be utilized to investigate vocal behavior, as well as potential limitations to this line of research. In the future, welfare scientists should attempt to identify reliable, valid species-specific calls that reflect emotional valence, which may be possible by adopting a dimensional approach. The dimensional approach considers both arousal and valence by comparing vocalizations emitted in negative and positive contexts. Ultimately, acoustic activity can be tracked continuously to detect shifts in welfare status or to evaluate the impact of animal transfers, introductions, and changes to the husbandry routine or environment. We encourage welfare scientists to expand their welfare monitoring toolkits by combining vocal activity with other behavioral measures and physiological biomarkers.

Opportunities for Regulatory Authorities to Assess Animal-Based Measures at the Slaughterhouse Using Sensor Technology and Artificial Intelligence: A Review

Animal-based measures (ABMs) are the preferred way to assess animal welfare. However, manual scoring of ABMs is very time-consuming during the meat inspection. Automatic scoring by using sensor technology and artificial intelligence (AI) may bring a solution. Based on review papers an overview was made of ABMs recorded at the slaughterhouse for poultry, pigs and cattle and applications of sensor technology to measure the identified ABMs. Also, relevant legislation and work instructions of the Dutch Regulatory Authority (RA) were scanned on applied ABMs. Applications of sensor technology in a research setting, on farm or at the slaughterhouse were reported for 10 of the 37 ABMs identified for poultry, 4 of 32 for cattle and 13 of 41 for pigs. Several applications are related to aspects of meat inspection. However, by European law meat inspection must be performed by an official veterinarian, although there are exceptions for the post mortem inspection of poultry. The examples in this study show that there are opportunities for using sensor technology by the RA to support the inspection and to give more insight into animal welfare risks. The lack of external validation for multiple commercially available systems is a point of attention.

An investigation of the use of ethyl chloride and meloxicam to decrease the pain associated with a single or double incision method of castration in piglets

Castration is a stressful and painful procedure that can impact swine welfare negatively. The objectives of this study were to (1) evaluate the effect of one incision compared to two incisions and the use of a topical vapocoolant (VAPO; ethyl chloride; a topical anesthetic) applied before castration and (2) evaluate the most effective combination in reducing pain in objective 1 and the use of Metacam®; meloxicam before castration on measures of performance, behavior, and physiology. Study 1 consisted of six treatment groups (N = 27 pigs per treatment) and included: nothing (NO); sham castrated (SH); one incision castration (C1); one incision castration plus VAPO (C1V); two incision castration (C2); two incision castration plus VAPO (C2V). Body weights and blood samples were taken at baseline and other time points after castration. Behavior measures were collected for 24 h after castration. Wound scores were collected daily for 10 days. The C1 pigs and C1V pigs were significantly heavier than the other castrated treatment groups but not different from NO and SH pigs. Vocalizations were louder for C1 and C1V pigs (P = 0.0015). Study 2 (N = 40 pigs per treatment) included: nothing (NO); one incision castration (C1); and one incision castration plus meloxicam administered 15 min before castration (C1M). The same measures (performance, behavior, and physiology) were collected as in Study 1. Performance measures and behavior did not differ among treatment groups. Physiological measures were only different for red blood cells (RBC; P = 0.0304). Pigs in C1 and C1M treatment groups had cortisol concentrations that were greater than the NO treatment group at 15 min post-castration (P < 0.05). The data collected give insight into the benefits of one-incision castration compared to 2-incision castration. However, the data only support a lower-level relief from acute pain associated with castration, as it is evident that pigs still experience stress at 15 min post-castration with or without the use of meloxicam. Further research could potentially identify the correct timing, route and dose for the administration of meloxicam.

Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

The assessment of animal welfare on-farm is important to ensure that current welfare standards are followed. The current manual assessment proposed by Welfare Quality® (WQ), although being an essential tool, is only a point-estimate in time, is very time consuming to perform, only evaluates a subset of the animals, and is performed by the subjective human. Automation of the assessment through information technologies (ITs) could provide a continuous objective assessment in real-time on all animals. The aim of the current systematic review was to identify ITs developed for welfare monitoring within the pig production chain, evaluate the ITs developmental stage and evaluate how these ITs can be related to the WQ assessment protocol. The systematic literature search identified 101 publications investigating the development of ITs for welfare monitoring within the pig production chain. The systematic literature analysis revealed that the research field is still young with 97% being published within the last 20 years, and still growing with 63% being published between 2016 and mid-2020. In addition, most focus is still on the development of ITs (sensors) for the extraction and analysis of variables related to pig welfare; this being the first step in the development of a precision livestock farming system for welfare monitoring. The majority of the studies have used sensor technologies detached from the animals such as cameras and microphones, and most investigated animal biomarkers over environmental biomarkers with a clear focus on behavioural biomarkers over physiological biomarkers. ITs intended for many different welfare issues have been studied, although a high number of publications did not specify a welfare issue and instead studied a general biomarker such as activity, feeding behaviour and drinking behaviour. The ‘good feeding’ principle of the WQ assessment protocol was the best represented with ITs for real-time on-farm welfare assessment, while for the other principles only few of the included WQ measures are so far covered. No ITs have yet been developed for the ‘Comfort around resting’ and the ‘Good human-animal relationship’ criteria. Thus, the potential to develop ITs for welfare assessment within the pig production is high and much work is still needed to end up with a remote solution for welfare assessment on-farm and in real-time.

Effect of Ventricular Folds on Vocalization Fundamental Frequency in Domestic Pigs (Sus scrofa domesticus)

This study investigates the effect of the ventricular folds on fundamental frequency (f_o) in the voice production of domestic pigs (Sus scrofa domesticus). The excised larynges of six subadult pigs were phonated in two preparation stages, with the ventricular folds present (PS1) and removed (PS2). Vocal fold resonances were tested with a laser vibrometer, and a four-mass computational model was created. Highly significant f_o differences were found between PS1 and PS2 (means at 93.7 and 409.3 Hz, respectively). Two tissue resonances were found at 115 Hz and 250-290 Hz. The computational model had unique solutions for abducted and adducted ventricular folds at about 150 and 400 Hz, roughly matching the f_o measured ex vivo for PS1 and PS2. The differing f_o encountered across preparation stages PS1 and PS2 is explained by distinct activation of either a high or a low eigenfrequency mode, depending on the engagement of the ventricular folds. The inability of the investigated larynges to vibrate at frequencies below 250 Hz in PS2 suggests that in vivo low-frequency calls of domestic pigs (pre-eminently grunts) are likely produced with engaged ventricular folds. Allometric comparison suggests that the special, mechanically coupled "double oscillator" has evolved to prevent signaling disadvantages. Given these traits, the porcine larynx might - apart from special applications relating to the involvement of ventricular folds - not be an ideal candidate for emulating human voice production in excised larynx experimentation.

Optimal Methods of Documenting Analgesic Efficacy in Neonatal Piglets Undergoing Castration

Analgesic products for piglet castration are critically needed. This requires extensive animal experimentation such as to meet regulatory-required proof of efficacy. At present, there are no validated methods of assessing pain in neonatal piglets. This poses challenges for investigators to optimize trial design and to meet ethical obligations to minimize the number of animals needed. Pain in neonatal piglets may be subtle, transient, and/or variably expressed and, in the absence of validated methods, investigators must rely on using a range of biochemical, physiological and behavioural variables, many of which appear to have very low (or unknown) sensitivity or specificity for documenting pain, or pain-relieving effects. A previous systematic review of this subject was hampered by the high degree of variability in the literature base both in terms of methods used to assess pain and pain mitigation, as well as in outcomes reported. In this setting we provide a narrative review to assist in determining the optimal methods currently available to detect piglet pain during castration and methods to mitigate castration-induced pain. In overview, the optimal outcome variables identified are nociceptive motor and vocal response scores during castration and quantitative sensory-threshold response testing and pain-associated behaviour scores following castration.

Automatic recording of individual oestrus vocalisation in group-housed dairy cattle: development of a cattle call monitor

Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate is known to increase during oestrus. The main challenge in using vocalisation to detect oestrus is correctly identifying the calling individual when animals are moving freely in large groups, as oestrus needs to be detected at an individual level. Therefore, we aimed to automate vocalisation recording and caller identification in group-housed dairy cows. This paper first presents the details of such a system and then presents the results of a pilot study validating its functionality, in which the automatic detection of calls from individual heifers was compared to video-based assessment of these calls by a trained human observer, a technique that has, until now, been considered the 'gold standard'. We developed a collar-based cattle call monitor (CCM) with structure-borne and airborne sound microphones and a recording unit and developed a postprocessing algorithm to identify the caller by matching the information from both microphones. Five group-housed heifers, each in the perioestrus or oestrus period, were equipped with a CCM prototype for 5 days. The recorded audio data were subsequently analysed and compared with audiovisual recordings. Overall, 1404 vocalisations from the focus heifers and 721 vocalisations from group mates were obtained. Vocalisations during collar changes or malfunctions of the CCM were omitted from the evaluation. The results showed that the CCM had a sensitivity of 87% and a specificity of 94%. The negative and positive predictive values were 80% and 96%, respectively. These results show that the detection of individual vocalisations and the correct identification of callers are possible, even in freely moving group-housed cattle. The results are promising for the future use of vocalisation in automatic oestrus detection systems.

Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners

Simple Summary

The increasing implementation of technological advances originally developed for video gaming (PlayStation, Xbox) is helping to progress livestock production so that it is both more efficient and more focused on the welfare of the animals. Such advances are necessary to ensure that innovations can emerge from applications using cameras, microphones and sensors to enhance the farmers’ eyes, ears and nose in everyday farming. This technology for remote monitoring of livestock, termed precision livestock farming, is the ability to automatically track individual livestock in real time. The goal of this review is to apprise swine veterinarians and their clientele on precision livestock farming with a general introduction to the technology available, a review of research and commercially available technology and the implications and opportunities for swine practitioners and farmers. Drawing from pig welfare criteria in the Common Swine Industry Audit, this review explains how these applications can be used to improve swine welfare within current pork production stakeholder expectations. Swine veterinarians and specialists, by virtue of their animal advocacy role, interpretation of benchmarking data, and stewardship in regulatory and commodity programs, can play a broader role in facilitating the transfer of precision livestock farming and technology to their clients.

Abstract

The burgeoning research and applications of technological advances are launching the development of precision livestock farming. Through sensors (cameras, microphones and accelerometers), images, sounds and movements are combined with algorithms to non-invasively monitor animals to detect their welfare and predict productivity. In turn, this remote monitoring of livestock can provide quantitative and early alerts to situations of poor welfare requiring the stockperson’s attention. While swine practitioners’ skills include translation of pig data entry into pig health and well-being indices, many do not yet have enough familiarity to advise their clients on the adoption of precision livestock farming practices. This review, intended for swine veterinarians and specialists, (1) includes an introduction to algorithms and machine learning, (2) summarizes current literature on relevant sensors and sensor network systems, and drawing from industry pig welfare audit criteria, (3) explains how these applications can be used to improve swine welfare and meet current pork production stakeholder expectations. Swine practitioners, by virtue of their animal and client advocacy roles, interpretation of benchmarking data, and stewardship in regulatory and traceability programs, can play a broader role as advisors in the transfer of precision livestock farming technology, and its implications to their clients.

Using self-organizing maps to classify humpback whale song units and quantify their similarity

Classification of vocal signals can be undertaken using a wide variety of qualitative and quantitative techniques. Using east Australian humpback whale song from 2002 to 2014, a subset of vocal signals was acoustically measured and then classified using a Self-Organizing Map (SOM). The SOM created (1) an acoustic dictionary of units representing the song's repertoire, and (2) Cartesian distance measurements among all unit types (SOM nodes). Utilizing the SOM dictionary as a guide, additional song recordings from east Australia were rapidly (manually) transcribed. To assess the similarity in song sequences, the Cartesian distance output from the SOM was applied in Levenshtein distance similarity analyses as a weighting factor to better incorporate unit similarity in the calculation (previously a qualitative process). SOMs provide a more robust and repeatable means of categorizing acoustic signals along with a clear quantitative measurement of sound type similarity based on acoustic features. This method can be utilized for a wide variety of acoustic databases especially those containing very large datasets and can be applied across the vocalization research community to help address concerns surrounding inconsistency in manual classification.

A Review of Pain Assessment in Pigs

There is a moral obligation to minimize pain in pigs used for human benefit. In livestock production, pigs experience pain caused by management procedures, e.g., castration and tail docking, injuries from fighting or poor housing conditions, “management diseases” like mastitis or streptococcal meningitis, and at parturition. Pigs used in biomedical research undergo procedures that are regarded as painful in humans, but do not receive similar levels of analgesia, and pet pigs also experience potentially painful conditions. In all contexts, accurate pain assessment is a prerequisite in (a) the estimation of the welfare consequences of noxious interventions and (b) the development of more effective pain mitigation strategies. This narrative review identifies the sources of pain in pigs, discusses the various assessment measures currently available, and proposes directions for future investigation.

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

Discerning pig screams in production environments

Pig vocalisations convey information about their current state of health and welfare. Continuously monitoring these vocalisations can provide useful information for the farmer. For instance, pig screams can indicate stressful situations. When monitoring screams, other sounds can interfere with scream detection. Therefore, identifying screams from other sounds is essential. The objective of this study was to understand which sound features define a scream. Therefore, a method to detect screams based on sound features with physical meaning and explicit rules was developed. To achieve this, 7 hours of labelled data from 24 pigs was used. The developed detection method attained 72% sensitivity, 91% specificity and 83% precision. As a result, the detection method showed that screams contain the following features discerning them from other sounds: a formant structure, adequate power, high frequency content, sufficient variability and duration.

A survey on the feasibility of sound classification on wireless sensor nodes

Wireless sensor networks are suitable to gain context awareness for indoor environments. As sound waves form a rich source of context information, equipping the nodes with microphones can be of great benefit. The algorithms to extract features from sound waves are often highly computationally intensive. This can be problematic as wireless nodes are usually restricted in resources. In order to be able to make a proper decision about which features to use, we survey how sound is used in the literature for global sound classification, age and gender classification, emotion recognition, person verification and identification and indoor and outdoor environmental sound classification. The results of the surveyed algorithms are compared with respect to accuracy and computational load. The accuracies are taken from the surveyed papers; the computational loads are determined by benchmarking the algorithms on an actual sensor node. We conclude that for indoor context awareness, the low-cost algorithms for feature extraction perform equally well as the more computationally-intensive variants. As the feature extraction still requires a large amount of processing time, we present four possible strategies to deal with this problem.

A vocal-based analytical method for goose behaviour recognition

Since human-wildlife conflicts are increasing, the development of cost-effective methods for reducing damage or conflict levels is important in wildlife management. A wide range of devices to detect and deter animals causing conflict are used for this purpose, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviours could form a critical component of a system capable of altering the disruptive stimuli to avoid this. In this paper we present a novel method to automatically recognise goose behaviour based on vocalisations from flocks of free-living barnacle geese (Branta leucopsis). The geese were observed and recorded in a natural environment, using a shielded shotgun microphone. The classification used Support Vector Machines (SVMs), which had been trained with labeled data. Greenwood Function Cepstral Coefficients (GFCC) were used as features for the pattern recognition algorithm, as they can be adjusted to the hearing capabilities of different species. Three behaviours are classified based in this approach, and the method achieves a good recognition of foraging behaviour (86-97% sensitivity, 89-98% precision) and a reasonable recognition of flushing (79-86%, 66-80%) and landing behaviour(73-91%, 79-92%). The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities are important. We conclude that vocalisations can be used to automatically detect behaviour of conflict wildlife species, and as such, may be used as an integrated part of a wildlife management system.

The use of Artificial Neural Networks to classify primate vocalizations: A pilot study on black lemurs

The identification of the vocal repertoire of a species represents a crucial prerequisite for a correct interpretation of animal behavior. Artificial Neural Networks (ANNs) have been widely used in behavioral sciences, and today are considered a valuable classification tool for reducing the level of subjectivity and allowing replicable results across different studies. However, to date, no studies have applied this tool to nonhuman primate vocalizations. Here, we apply for the first time ANNs, to discriminate the vocal repertoire in a primate species, Eulemur macaco macaco. We designed an automatic procedure to extract both spectral and temporal features from signals, and performed a comparative analysis between a supervised Multilayer Perceptron and two statistical approaches commonly used in primatology (Discriminant Function Analysis and Cluster Analysis), in order to explore pros and cons of these methods in bioacoustic classification. Our results show that ANNs were able to recognize all seven vocal categories previously described (92.5-95.6%) and perform better than either statistical analysis (76.1-88.4%). The results show that ANNs can provide an effective and robust method for automatic classification also in primates, suggesting that neural models can represent a valuable tool to contribute to a better understanding of primate vocal communication. The use of neural networks to identify primate vocalizations and the further development of this approach in studying primate communication are discussed.

The gradual vocal responses to human-provoked discomfort in farmed silver foxes

Vocal indicators of welfare have proven their use for many farmed and zoo animals and may be applied to farmed silver foxes as these animals display high vocal activity toward humans. Farmed silver foxes were selected mainly for fur, size, and litter sizes, but not for attitudes to people, so they are fearful of humans and have short-term welfare problems in their proximity. With a human approach test, we designed here the steady increase and decrease of fox-human distance and registered vocal responses of 25 farmed silver foxes. We analyzed the features of vocalizations produced by the foxes at different fox-human distances, assuming that changes in vocal responses reflect the degrees of human-related discomfort. For revealing the discomfort-related vocal traits in farmed silver foxes, we proposed and tested the algorithm of "joint calls," equally applicable for analysis of all calls independently on their structure, either tonal or noisy. We discuss that the increase in proportion of time spent vocalizing and the shift of call energy toward higher frequencies may be integral vocal characteristics of short-term welfare problems in farmed silver foxes and probably in other captive mammals.

Vocal-type classification as a tool to identify stress in piglets under on-farm conditions

Previous studies have shown that the analysis of high frequency stress calls in pigs can serve as a reliable tool in welfare research. Our study focuses on the classification of three different classes of piglet vocalisation: grunting, squealing and screaming. In a castration experiment (Experiment 1), 3,285 vocalisations from 42 piglets were analysed for 21 different vocal characteristics. A first discriminant function for the three vocal types was derived from recordings made under laboratory-like conditions. A second discriminant function was derived from non-calibrated measurements of the relative sound energy content. These two classifications revealed 86.7% identical assignments of vocalisations to the three vocal types. The second classification allowed for vocalisation analyses of animals under on-farm recording conditions. This technique was validated during an open-field test (Experiment 2) with piglets housed in two different farrowing systems (11,089 vocalisations, 22 piglets). The proportion of screaming sounds was lower for piglets from a group-farrowing (GF) system than for those from a single-farrowing (SF) system. Sound properties showed differences between as well as within the two experiments for all three vocal types. Vocalisations from SF and GF piglets differed significantly in the duration, energy, and relative maximum levels. We conclude that vocal-type analysis can not only help to identify vocalisation indicative of pain during castration, but also vocal behaviour changes indicating separation stress during the open-field test. Therefore, classification of vocal types can add valuable information to studies that use pig vocalisation for the assessment of welfare.

Time-series analysis for online recognition and localization of sick pig (Sus scrofa) cough sounds

This paper considers the online localization of sick animals in pig houses. It presents an automated online recognition and localization procedure for sick pig cough sounds. The instantaneous energy of the signal is initially used to detect and extract individual sounds from a continuous recording and their duration is used as a preclassifier. Autoregression (AR) analysis is then employed to calculate an estimate of the sound signal, and the parameters of the estimated signal are subsequently evaluated to identify the sick cough sounds. It is shown that the distribution of just three AR parameters provides an adequate classifier for sick pig coughs. A localization technique based on the time difference of arrival is evaluated on field data and is shown that it is of acceptable accuracy for this particular application. The algorithm is applied on continuous recordings from a pig house to evaluate its effectiveness. The correct identification ratio ranged from 73% (27% false positive identifications) to 93% (7% false positive identifications) depending on the position of the microphone that was used for the recording. Although the false negative identifications are about 50% it is shown that this accuracy can be enough for the purpose of this tool. Finally, it is suggested that the presented application can be used to online monitor the welfare in a pig house, and provide early diagnosis of a cough hazard and faster treatment of sick animals.

Vocal fold elasticity of the Rocky Mountain elk (Cervus elaphus nelsoni) - producing high fundamental frequency vocalization with a very long vocal fold

The vocal folds of male Rocky Mountain elk (Cervus elaphus nelsoni) are about 3 cm long. If fundamental frequency were to be predicted by a simple vibrating string formula, as is often done for the human larynx, such long vocal folds would bear enormous stress to produce the species-specific mating call with an average fundamental frequency of 1 kHz. Predictions would be closer to 50 Hz. Vocal fold histology revealed the presence of a large vocal ligament between the vocal fold epithelium and the thyroarytenoid muscle. In tensile tests, the stress-strain response of vocal fold epithelium and the vocal ligament were determined. Elasticity of both tissue structures reached quantitative values similar to human tissue. It seems unlikely that the longitudinal stress in elk vocal folds can exceed that in human vocal folds by an order of magnitude to overcome the drop in fundamental frequency due to a 3:1 increase in vocal fold length. Alternative hypotheses of how the elk produces high fundamental frequency utterances, despite its very long vocal fold, include a reduced effective vocal fold length in vibration, either due to bending properties along the vocal fold, or by actively moving the boundary point with muscle stiffening. The relationships between an individual's average fundamental frequency, vocal fold length and body size are discussed.

Vocalisations and acoustic parameters of flock noise from feather pecking and non-feather pecking laying flocks

1. In this study, the calling rates of vocalisations known to indicate distress and aversive events (Alarm calls, Squawks, Total vocalisations) and acoustic parameters of flock noise were quantified from feather and non-feather pecking laying flocks. 2. One hour of flock noise (background machinery and hen vocalisations) was recorded from 21 commercial free-range laying hen flocks aged > or =35 weeks. Ten of the flocks were classified as feather pecking (based on a plumage condition score) and 11 as non-feather pecking. 3. Recordings were made using a Sony DAT recorder and Audio-Technica omni-directional microphone, placed in the centre of the house-1.5 m from the ground. Avisoft-SASlab Pro was used to create and analyse audio spectrograms. 4. There was no effect of flock size or farm on call/s or acoustic parameters of flock noise. However, strain had an effect on the number of Total vocalisation/s; the Hebden Black flock made more calls than Lohmann flocks. Feather pecking flocks gave more Squawk/s and more Total vocalisation/s than non-feather pecking flocks. Feather pecking did not explain variation in alarm call rate or, intensity (dB) and frequency (Hz) measures of flock noise. 5. The differences between Squawk and Total vocalisation call rates of feather and non-feather pecking flocks are a new finding. An increase or change in flock calling rate may be evident before other conventional measures of laying hen welfare such as a drop in egg production or increase in plumage damage, thus enabling farmers to make management or husbandry changes to prevent an outbreak of feather pecking.

Acetylcholine injection into the amygdala elicits vocalization in domestic pigs (Sus scrofa)

In a pilot study we have injected the amygdala of five female pigs (age 8 weeks) with acetylcholine (ACh, 5.5muM/20mul) and recorded short latency utterances. The evoked vocalizations displayed the characteristics of natural screams in sonagram appearance and hearing impression. Quantitative analyses, too, revealed the similarity of the ACh-evoked vocalizations with natural screaming. The results demonstrate for the first time that aversive vocalizations can be triggered by cholinergic amygdala stimulation.

Artificial neural network discrimination of black-capped chickadee (Poecile atricapillus) call notes

Artificial neural networks were trained to discriminate between two different notes from the "chick-a-dee" call of the black-capped chickadee (Poecile atricapillus). An individual note was represented as a vector of nine summary features taken from note spectrograms. A network was trained to respond to exemplar notes of one type (e.g., A notes) and to fail to respond to exemplar notes of another type (e.g., B notes). After this training, the network was presented novel notes of the two different types, as well as notes of the same two types that had been shifted upwards or downwards in frequency. The strength of the response of the network to each novel and shifted note was recorded. When network responses were plotted as a function of the degree of frequency shift, the results were very similar to those observed in birds that were trained in an analogous task [Charrier et al., J. Comp. Psychol. 119(4), 371-380 (2005)]. The implications of these results to simulating behavioral studies of animal communication are discussed.

Automated recording of stress vocalisations as a tool to document impaired welfare in pigs

The vocalisations of animals are results of particular emotional states. For example, the stress screams of pigs may be indicators of disturbed welfare. Our objective was to develop a system to monitor and record levels of stress calls in pigs, which could be employed in environments of breeding, transportation and slaughter. Using a combination of sound analysis by linear prediction coding and artificial neural networks, it was possible to detect the stress vocalisations of pigs in noisy pig units with few recognition errors (<5%). The system (STREMODO: stress monitor and documentation unit) running on PCs is insensitive to environmental noise, human speech and pig vocalisations other than screams. As a stand-alone device it can be routinely used for the objective, non-invasive measurement of acute stress in various farming environments. The system delivers reliable, reproducible registrations of stress vocalisations. Its detection quality in commercial systems was found to correlate well with that of human experts. STREMODO is particularly well-suited for comparisons of housing and management regimes. Since the system can be trained to recognise various animal vocalisations, its use with other species is also well within its scope.