@article {58170,
	title = {A two-stage approach to automatically detect and classify woodpecker (Fam. Picidae) sounds},
	journal = {Applied Acoustics},
	volume = {166},
	year = {2020},
	month = {Jan-09-2020},
	pages = {107312},
	abstract = {<p>Inventorying and monitoring which bird species inhabit a specific area give rich and reliable information regarding its conservation status and other meaningful biological parameters. Typically, this surveying process is carried out manually by ornithologists and birdwatchers who spend long periods of time in the areas of interest trying to identify which species occur. Such methodology is based on the experts\&rsquo; own knowledge, experience, visualization and hearing skills, which results in an expensive, subjective and error prone process. The purpose of this paper is to present a computing friendly system able to automatically detect and classify woodpecker acoustic signals from a real-world environment. More specifically, the proposed architecture features a two-stage Learning Classifier System that uses (1) Mel Frequency Cepstral Coefficients and Zero Crossing Rate to detect bird sounds over environmental noise, and (2) Linear Predictive Cepstral Coefficients, Perceptual Linear Predictive Coefficients and Mel Frequency Cepstral Coefficients to identify the bird species and sound type (i.e., vocal sounds such as advertising calls, excitement calls, call notes and drumming events) associated to that bird sound. Conducted experiments over a data set of the known woodpeckers species belonging to the Picidae family that live in the Iberian peninsula have resulted in an overall accuracy of 94,02\%, which endorses the feasibility of this proposal and encourage practitioners to work toward this direction.</p>
},
	keywords = {Audio classification, birdsong, Birdsound classification, event detection, Woodpeckers},
	issn = {0003682X},
	doi = {10.1016/j.apacoust.2020.107312},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0003682X19305080},
	author = {Vida{\~n}a-Vila, Ester and Navarro, Joan and Alsina-Pag{\`e}s, Rosa Ma and Ram{\'\i}rez, {\'A}lvaro}
}
@article {47717,
	title = {On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts},
	journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
	volume = {25},
	year = {2017},
	month = {Jan-06-2017},
	pages = {1193 - 1206},
	abstract = {<p>We introduce a novel approach to studying animal behavior and the context in which it occurs, through the use of microphone backpacks carried on the backs of individual free-flying birds. These sensors are increasingly used by animal behavior researchers to study individual vocalizations of freely behaving animals, even in the field. However, such devices may record more than an animal\&$\#$39;s vocal behavior, and have the potential to be used for investigating specific activities (movement) and context (background) within which vocalizations occur. To facilitate this approach, we investigate the automatic annotation of such recordings through two different sound scene analysis paradigms: A scene-classification method using feature learning, and an event-detection method using probabilistic latent component analysis. We analyze recordings made with Eurasian jackdaws (Corvus monedula) in both captive and field settings. Results are comparable with the state of the art in sound scene analysis; we find that the current recognition quality level enables scalable automatic annotation of audio logger data, given partial annotation, but also find that individual differences between animals and/or their backpacks limit the generalization from one individual to another. we consider the interrelation of \&ldquo;scenes\&rdquo; and \&ldquo;events\&rdquo; in this particular task, and issues of temporal resolution.</p>
},
	keywords = {acoustics, birds, context, event detection, image analysis, microphones, pattern recognition, speech, zoology},
	issn = {2329-9290},
	doi = {10.1109/TASLP.2017.2690565},
	url = {http://ieeexplore.ieee.org/document/7933044/},
	author = {Stowell, Dan and Gill, Lisa F.}
}
