Spying on small wildlife sounds using affordable collar-mounted miniature microphones: an innovative method to record individual daylong vocalisations in chipmunks

Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag

Timely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, incomplete feature selection, and poor recognition accuracy. To overcome these difficulties, this study proposes a sound detection method for cows in oestrus based on machine learning technology using an optimal feature combination and an optimal time window. Firstly, a dual-channel sound detection tag consisting of a unidirectional microphone and an omnidirectional microphone (OM) was developed. The Least Mean Squares adaptive algorithm based on wavelet thresholds was used to filter the signals from the OM, and the dual-channel endpoint detection algorithm was used to identify the lowing of individual cows. The Friedman analysis was then used to select the sound features with significant differences before and after oestrus in terms of time, frequency, and cepstrum, and these were used to determine the most acceptable feature combination. We then analysed the effects of Back Propagation Neural Network (BPNN), Cartesian Regression Tree, Support Vector Machine, and Random Forest classification on the accuracy, precision, sensitivity, specificity, and F1 score of oestrus discrimination. Different time windows were used, and the discrimination performance of these algorithms was evaluated using the Area under Receiver Operating Characteristic Curve to find the most satisfactory match between the time window and the recognition algorithm. The dual-channel acoustic tag's accuracy, precision, sensitivity, and specificity results were 91.25, 98.83, 91.75, and 83.68%, respectively. BPNN with the 70 ms time window and the feature combination (spectral roll-off + spectral flatness + Mel-Frequency Cepstrum Coefficients) was confirmed as the most suitable oestrus recognition method. The average accuracy, precision, sensitivity, specificity, and F1 score of this method were 97.62, 98.07, 97.17, 97.19, and 97.63%, respectively. Based on these results, the approach was shown to be a feasible means of oestrus detection in dairy cows. Based on its ability to differentiate cows and its consistency, it was demonstrated that sound has the potential to replace accelerometers as an early indicator of oestrus in dairy cows.

Toward Passive Acoustic Monitoring of Lemurs: Using an Affordable Open-Source System to Monitor Phaner Vocal Activity and Density

Developing new cost-effective methods for monitoring the distribution and abundance of species is essential for conservation biology. Passive acoustic monitoring (PAM) has long been used in marine mammals and has recently been postulated to be a promising method to improve monitoring of terrestrial wildlife as well. Because Madagascar’s lemurs are among the globally most threatened taxa, this study was designed to assess the applicability of an affordable and open-source PAM device to estimate the density of pale fork-marked lemurs (Phaner pallescens). Using 12 playback experiments and one fixed transect of four automated acoustic recorders during one night of the dry season in Kirindy Forest, we experimentally estimated the detection space for Phaner and other lemur vocalizations. Furthermore, we manually annotated more than 10,000 vocalizations of Phaner from a single location and used bout rates from previous studies to estimate density within the detection space. To truncate detections beyond 150 m, we applied a sound pressure level (SPL) threshold filtering out vocalizations below SPL 50 (dB re 20 μPa). During the dry season, vocalizations of Phaner can be detected with confidence beyond 150 m by a human listener. Within our fixed truncated detection area corresponding to an area of 0.07 km2 (detection radius of 150 m), we estimated 10.5 bouts per hour corresponding to a density of Phaner of 38.6 individuals/km2. Our density estimates are in line with previous estimates based on individually marked animals conducted in the same area. Our findings suggest that PAM also could be combined with distance sampling methods to estimate densities. We conclude that PAM is a promising method to improve the monitoring and conservation of Phaner and many other vocally active primates.

Quantifying song behavior in a free-living, light-weight, mobile bird using accelerometers

To acquire a fundamental understanding of animal communication, continuous observations in a natural setting and at an individual level are required. Whereas the use of animal-borne acoustic recorders in vocal studies remains challenging, light-weight accelerometers can potentially register individuals' vocal output when this coincides with body vibrations. We collected one-dimensional accelerometer data using light-weight tags on a free-living, crepuscular bird species, the European Nightjar (Caprimulgus europaeus). We developed a classification model to identify four behaviors (rest, sing, fly, and leap) from accelerometer data and, for the purpose of this study, validated the classification of song behavior. Male nightjars produce a distinctive "churring" song while they rest on a stationary song post. We expected churring to be associated with body vibrations (i.e., medium-amplitude body acceleration), which we assumed would be easy to distinguish from resting (i.e., low-amplitude body acceleration). We validated the classification of song behavior using simultaneous GPS tracking data (i.e., information on individuals' movement and proximity to audio recorders) and vocal recordings from stationary audio recorders at known song posts of one tracked individual. Song activity was detected by the classification model with an accuracy of 92%. Beyond a threshold of 20 m from the audio recorders, only 8% of the classified song bouts were recorded. The duration of the detected song activity (i.e., acceleration data) was highly correlated with the duration of the simultaneously recorded song bouts (correlation coefficient = 0.87, N = 10, S = 21.7, p = .001). We show that accelerometer-based identification of vocalizations could serve as a promising tool to study communication in free-living, small-sized birds and demonstrate possible limitations of audio recorders to investigate individual-based variation in song behavior.

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.

Using on-board sound recordings to infer behaviour of free-moving wild animals

Technological advances in the last 20 years have enabled researchers to develop increasingly sophisticated miniature devices (tags) that record an animal's behaviour not from an observational, external viewpoint, but directly on the animals themselves. So far, behavioural research with these tags has mostly been conducted using movement or acceleration data. But on-board audio recordings have become more and more common following pioneering work in marine mammal research. The first questions that come to mind when recording sound on-board animals concern their vocal behaviour. When are they calling? How do they adjust their behaviour? What acoustic parameters do they change and how? However, other topics like foraging behaviour, social interactions or environmental acoustics can now be addressed as well and offer detailed insight into the animals' daily life. In this Review, we discuss the possibilities, advantages and limitations of on-board acoustic recordings. We focus primarily on bats as their active-sensing, echolocating lifestyle allows many approaches to a multi-faceted acoustic assessment of their behaviour. The general ideas and concepts, however, are applicable to many animals and hopefully will demonstrate the versatility of on-board acoustic recordings and stimulate new research.

Does audience affect the structure of warble song in budgerigars (Melopsittacus undulatus)?

In many bird species, male song functions both to defend a territory against other males and to attract a female mate. Male budgerigars (Melopsittacus undulatus) produce a song-like vocal signal, the warble, that can be directed at either females or other males. Warble is a long, complex, low amplitude, and variable vocalization composed of different element types. While there is some evidence that warble can induce reproduction, the function of this signal is largely uncertain and it is unclear whether male- and female-directed warble differ in either function or structure. We recorded male budgerigars in the presence of either their mate or a familiar male to identify whether the warbles produced with different audiences differed in structure. We dissected each warble into specific element units, classified units into a limited number of types by rule-based visual classification and calculated the proportion of each element type, element diversity, and total duration for the male- and female-directed warbles of each male. We also examined the sequential organization of warble element types (syntax) using time-window lagged sequential analysis. We found no differences in the proportions of different elements used, element diversity or duration of warbles between male- and female-directed warble. The syntax of warbles is similar when directed at males or females. However, we found greater between-individual similarity in the sequential organization of warbles directed towards females than in those directed towards males. The greater syntactical consistency in female-directed warble suggests that females may prefer either specific types of element sequences, or consistency itself, and thus shape the organization of warbles.

Seabird acoustic communication at sea: a new perspective using bio-logging devices

Most seabirds are very noisy at their breeding colonies, when aggregated in high densities. Calls are used for individual recognition and also emitted during agonistic interactions. When at sea, many seabirds aggregate over patchily distributed resources and may benefit from foraging in groups. Because these aggregations are so common, it raises the question of whether seabirds use acoustic communication when foraging at sea? We deployed video-cameras with built in microphones on 36 Cape gannets (Morus capensis) during the breeding season of 2010–2011 at Bird Island (Algoa Bay, South Africa) to study their foraging behaviour and vocal activity at sea. Group formation was derived from the camera footage. During ~42 h, calls were recorded on 72 occasions from 16 birds. Vocalization exclusively took place in the presence of conspecifics, and mostly in feeding aggregations (81% of the vocalizations). From the observation of the behaviours of birds associated with the emission of calls, we suggest that the calls were emitted to avoid collisions between birds. Our observations show that at least some seabirds use acoustic communication when foraging at sea. These findings open up new perspectives for research on seabirds foraging ecology and their interactions at sea.