Automated bioacoustics: methods in ecology and conservation and their potential for animal welfare monitoring

Mcloughlin, Michael P.; Stewart, Rebecca; McElligott, Alan G.

doi:10.1098/rsif.2019.0225

Cited by 98 publications

(77 citation statements)

References 140 publications

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“…Equation (2) showed a highly significant negative correlation between weight and PF. These results are in agreement with results reported for turkeys by Liu et al [16] and reinforce findings of previous studies that reported changes in animal vocalizations as the animals grow [28]. The PF of broiler chicken vocalizations also is very highly correlated ((p < 0.0001)) with the age and weight of birds [17], permitting the use of PF to predict changes in bird weight.…”

Section: Weight-sound Relationshipsupporting

confidence: 91%

Sound Analysis to Predict the Growth of Turkeys

Abdel-Kafy

Ibraheim

et al. 2020

Animals

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Protocols for manual weighing of turkeys are not practical on turkey farms because of the large body sizes, heavy weights and flighty nature of turkeys. The sounds turkeys make may be a proxy for bird weights, but the relationship between turkey sounds and bird weights has not been studied. The aim of this study was to correlate peak frequency (PF) of vocalization with the age and weight of the bird and examine the possibility using PF to predict the weight of turkeys. The study consisted of four trials in Egypt. Sounds of birds and their weights were recorded for 11 days during the growth period in each trial. A total 2200 sounds were manually analyzed and labelled by extracting individual and general sounds on the basis of the amplitude and frequency of the sound signal. The PF of vocalizations in each trial, as well as in pooled trails, were evaluated to determine the relationship between PF and the age and weight of the turkey. PF exhibited a highly significant negative correlation with the weight and age of the turkeys showing that PF of vocalizations can be used for predicting the weight of turkeys. Further studies are necessary to refine the procedure.

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Section: Weight-sound Relationshipsupporting

confidence: 91%

Sound Analysis to Predict the Growth of Turkeys

Abdel-Kafy

Ibraheim

et al. 2020

Animals

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“…Livestock vocalizations can be a good source of information about animal welfare status and social interactions (Exadaktylos et al, 2014;Neethirajan, 2017). Acoustic sensing is a non-invasive method, inexpensive, and less dependent on lighting or the specific position of the animal (Mcloughlin et al, 2019). Some studies have investigated the relationship between vocalization and health (Exadaktylos et al, 2008;Silva et al, 2009;Ferrari et al, 2010), poultry welfare (Zimmerman and Koene, 1998), stress events [e.g., piglet crushing (Manteuffel et al, 2017), pain during husbandry procedures (Marchant-Forde et al, 2009)], and feeding behavior based on pecking sound (Aydin et al, 2014).…”

Section: Activity Sensors (Accelerometers Activity Monitors)mentioning

confidence: 99%

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

et al. 2020

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Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and

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“…Though some studies have used specific algorithms to classify the notes into distinct categories, these algorithms' efficiency and precision are uncertain (Pourhomayoun et al, 2013;Mcloughlin, Stewart, & McElligott, 2019;Rao, Montgomery, Garg, & Charleston, 2020).…”

Section: Comparison With Other Song Complexity Measuresmentioning

confidence: 99%

Spectrogram cross-correlation can be used to measure the complexity of bird vocalizations

Sawant

Arvind

Joshi

et al. 2021

Preprint

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Birdsong is an important signal in mate attraction and territorial defense. Quantifying the complexity of these songs can shed light on individual fitness, sexual selection, and behavior. Several techniques have been used to quantify song complexity and be broadly categorized into diversity indices, measures of stationary probabilities, and measures of sequential variations. However, these methods are unable to account for important acoustic features like the frequency bandwidth and the variety in the shape of syllables which are an integral part of these vocal signals. This study proposes a new complexity measure that considers intra-song note variability and calculates a weighted index for birdsongs using spectral cross-correlation. We compared the previously described methods to understand the advantages and limitations based on the factors that would be affecting the complexity of songs. We developed a new method- Note Variability Index (NVI), which incorporates the spectral features of notes while quantifying complexity. This measure alleviates the need for manual annotations of notes that can be error-prone. We used Spectrogram Cross-Correlation (SPCC) to compare notes within a song and used the output values to quantify song complexity. To check for the efficacy of the new method, we generated synthetic songs to caricature extremes in song complexity and compared selected conventional complexity measures along with the NVI. We provide case-specific limitations of these methods. Additionally, to examine the efficacy of this new method in real-world scenarios, we used natural birdsongs from multiple species across the globe with varying song structures to compare conventional methods with NVI. To our knowledge, NVI is the only song complexity method that captures the variation of spectral features of notes in songs where the conventional methods fail to distinguish between similar song structures with different note types. As NVI does not need a manual classification of notes, it can be easily implemented for any type of birdsong with existing sound analysis softwares; it is very quick, avoids the possible biases in note classification, and can possibly be automated for large datasets in the future.

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Automated bioacoustics: methods in ecology and conservation and their potential for animal welfare monitoring

Cited by 98 publications

References 140 publications

Sound Analysis to Predict the Growth of Turkeys

Sound Analysis to Predict the Growth of Turkeys

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

Spectrogram cross-correlation can be used to measure the complexity of bird vocalizations

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