Automatic cough detection for bovine respiratory disease in a calf house

Abstract

In calf rearing, bovine respiratory disease (BRD) is a major animal health challenge. Farmers incur severe economic losses due to BRD. Additional to economic costs, outbreaks of BRD impair the welfare of the animal and extra expertise and labour are needed to treat and care for the infected animals. Coughing is recognised as a clinical manifestation of BRD. Therefore, the monitoring of coughing in a calf house has the potential to detect cases of respiratory infection before they become too severe, and thus to limit the impact of BRD on both the farmer and the animal. The objective of this study was to develop an algorithm for detection of coughing sounds in a calf house. Sounds were recorded in four adjacent compartments of one calf house over two time periods (82 and 96 days). There were approximately 21 and 14 calves in each compartment over the two time-periods, respectively. The algorithm was developed using 445 min of sound data. These data contained 664 different cough references, which were labelled by a human expert. It was found that, during the first time period in all 3 of the compartments and during the second period in 2 out of 4 compartments, the algorithm worked very well (precision higher than 80%), while in the 2 other cases the algorithm worked well but the precision was less (66.6% and 53.8%). A relation between the number of calves diagnosed with BRD and the detected coughs is shown.

Assessment of Laying Hens’ Thermal Comfort Using Sound Technology

“…In addition, manual labelling was used to score the quality of measured data corresponding to 3 ratings [34]. Overlapped sounds labelled as rating 1 were not selected as training and testing datasets due to their complex acoustic features and limitations in sound source separation technology.…”

“…The objective was to detect the calls of hens. A good selection of each sound event such as the exact beginning and end, will improve the success rate of the classification algorithm [34]. Filtered hen calls contain more energy than the background noise, so the envelope of energy method was selected to perform the sound detection.…”

“…Filtered hen calls contain more energy than the background noise, so the envelope of energy method was selected to perform the sound detection. This automatic sound event selection method extracted endpoints from a continuous recording based on a manually selected threshold (0.35 in this trial) [34,35]. The algorithm was gradually optimised by maximising the overlap factor between the algorithm output and the labelled sound [36].…”

Field-Applicable Pig Anomaly Detection System Using Vocalization for Embedded Board Implementations

“…Recently, several studies have been reported to detect abnormalities in livestock using sound sensors to guarantee animals' health and welfare without affecting farm budgets. These methods are not only cost effective, as they are cheaper than other sensors, but also more practical and animal friendly because they collect data constantly for 24 h a day without causing any stress or discomfort to the animals [2][3][4][5][6][7][8]. In the case of chickens, certain studies that employed sound data include a study of hens' stress caused by temperature changes based on the results of vocalization analysis [2]; studies on the detection of respiratory diseases based on broilers' sneezing and coughing [3,4]; and a study that attempted to detect avian influenza using chicken sound data [5].…”

“…In the case of chickens, certain studies that employed sound data include a study of hens' stress caused by temperature changes based on the results of vocalization analysis [2]; studies on the detection of respiratory diseases based on broilers' sneezing and coughing [3,4]; and a study that attempted to detect avian influenza using chicken sound data [5]. Similarly, in the case of cattle, certain studies employed sound to detect cows' estrous [6] and respiratory diseases [7,8]. In conclusion, sound data are important as they allow retrieving information that can be useful for detecting abnormalities in livestock.…”