@article {58000,
	title = {Preliminary Acoustic Analysis of Farm Management Noise and Its Impact on Broiler Welfare},
	journal = {Proceedings of the Royal Society B: Biological Sciences},
	volume = {6},
	year = {2019},
	abstract = {<p>Farm management practices done by machinery generate a high acoustical impact on animals. The acoustic variations in terms of equivalent level (Leq) and the different types of noise can affect the well-being of broilers by means of reducing the food and water ingest. In this work, we create a dataset in which we conduct a preliminary analysis of the acoustical impact generated by the farm management in a intensive broiler poultry farm of 25,000 birds. The project collects acoustic data during the first two weeks of the birds life, focusing the study on the first week. To create the dataset we randomly select some files from each day of the study and they are analysed and labelled manually using an audio analysis software. The acoustical events defined in collaboration with the farmer and vet are fan, food and water supply, based on duration, impact and Signal to Noise Ratio (SNR). The analysis concludes that the main acoustical source in a broilers\&rsquo; farm is fan, and that it has an non negligible acoustical impact. Nevertheless, the most frequent acoustical noise source active is food supply, but with less Leq impact.</p>
},
	keywords = {acoustic impact, broiler well-fare, farm management noise, Leq, poultry farm},
	author = {Oliinychenko, DmytroGerardo Jos{\'e} Ginovart-Panisello and Rosa Ma Alsina-Pag{\`e}s}
}
@article {57923,
	title = {Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls},
	year = {2019},
	abstract = {<p>Poultry is one of the most strategic source of human foods. There have been seen some hopeful signs of bioacoustics application to monitor the health condition of this vital food source. One of the obstacles is that the bird\&rsquo;s call is combined with some unvoiced sounds and extracting the calls is not easy, especially when the bird is sick. This research is a report on successful application of some of the features involved in extracting healthy and non-healthy birds\&rsquo; calls from their sound signals. One hundred and twenty birds from two genotypes \&ndash; Ross and Cobb \&ndash; were placed in two groups, a control and those challenged with respiratory diseases. They were reared and their sound was recorded daily. The vocal phrases of the recorded audio signals were extracted using the presented algorithm. Results of analysis showed that an increase in age and onset of illness are two factors that cause an error increase. Detection accuracy was calculated at 95\% for healthy young birds and 72\% for non-healthy birds. A significant part of this error is due to misclassing the calls as non-vocal segments. This meant that 97\% of the activities classified as vocal phrases were, in fact, vocal. These results showed that the idea of such an easy-to-implement algorithm could potentially be employed for the coarselevel segmentation of some animal vocalization signals with reliable outputs, which is an essential and primary step in bioacoustics research.</p>
},
	keywords = {Audio features, bioacoustics, Gallus gallus, Health monitoring, Respiratory diseases},
	doi = {10.1016/j.compag.2019.105100},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0168169918305982},
	author = {Mahdavian, Alireza and Minaei, Saeid and Yang, Ce and Almasganj, Farshad and Rahimi, Shaban and Marchetto, Peter M.}
}
