Next Article in Journal
Four Datasets Derived from an Archive of Personal Homepages (1995–2009)
Previous Article in Journal
Transcriptome Dataset of Soybean (Glycine max) Grown under Phosphorus-Deficient and -Sufficient Conditions
Article Menu

Export Article

Open AccessData Descriptor
Data 2017, 2(2), 18; doi:10.3390/data2020018

Towards Automatic Bird Detection: An Annotated and Segmented Acoustic Dataset of Seven Picidae Species

GTM—Grup de recerca en Tecnologies Mèdia. La Salle—Universitat Ramon Llull. C/Quatre Camins, 30, 08022 Barcelona, Catalonia, Spain
GRITS—Grup de Recerca en Internet Technologies & Storage. La Salle—Universitat Ramon Llull, C/Quatre Camins, 30, 08022 Barcelona, Catalonia, Spain
Author to whom correspondence should be addressed.
Academic Editor: Jamal Jokar Arsanjani
Received: 20 April 2017 / Revised: 12 May 2017 / Accepted: 13 May 2017 / Published: 16 May 2017
View Full-Text   |   Download PDF [9858 KB, uploaded 16 May 2017]   |  


Analysing behavioural patterns of bird species in a certain region enables researchers to recognize forthcoming changes in environment, ecology, and population. Ornithologists spend many hours observing and recording birds in their natural habitat to compare different audio samples and extract valuable insights. This manual process is typically undertaken by highly-experienced birders that identify every species and its associated type of sound. In recent years, some public repositories hosting labelled acoustic samples from different bird species have emerged, which has resulted in appealing datasets that computer scientists can use to test the accuracy of their machine learning algorithms and assist ornithologists in the time-consuming process of analyzing audio data. Current limitations in the performance of these algorithms come from the fact that the acoustic samples of these datasets combine fragments with only environmental noise and fragments with the bird sound (i.e., the computer confuses environmental sound with the bird sound). Therefore, the purpose of this paper is to release a dataset lasting more than 4984 s that contains differentiated samples of (1) bird sounds and (2) environmental sounds. This data descriptor releases the processed audio samples—originally obtained from the Xeno-Canto repository—from the known seven families of the Picidae species inhabiting the Iberian Peninsula that are good indicators of the habitat quality and have significant value from the environment conservation point of view. View Full-Text
Keywords: birdsong; acoustic bird recognition; audio sample; dataset; birdsong dataset birdsong; acoustic bird recognition; audio sample; dataset; birdsong dataset

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Vidaña-Vila, E.; Navarro, J.; Alsina-Pagès, R.M. Towards Automatic Bird Detection: An Annotated and Segmented Acoustic Dataset of Seven Picidae Species. Data 2017, 2, 18.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics



[Return to top]
Data EISSN 2306-5729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top