Automatic classification of bird species based on their vocalizations is a topic of crucial relevance for the research conducted by biologists, ornithologists, ecologists, and related disciplines. This work in concentrated on nocturnal species; even though the analysis of their population trends is a key indicator, there is a gap in the literature addressing their audio-based identification. After compiling a suitable dataset including six nocturnal bird species, this study employs both supervised (k-Nearest Neighbors, Support Vector Machines) and unsupervised (k-means) methods operating in the feature space formed by Mel-spectrograms. We conclude that automatic classification based on k-Nearest Neighbour and Support Vector Machines provide almost excellent results with a recognition rate in the order of 90%.
Acoustic Identification of Nocturnal Bird Species / M. Acconcjaioco, S. Ntalampiras (COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE). - In: Advances in signal processing and intelligent recognition systems / [a cura di] S.M. Thampi, R.M. Hegde, S. Krishnan, J. Mukhopadhyay, V. Chaudhary, O. Marques, S. Piramuthu, J.M. Corchado. - [s.l] : Springer, 2020. - ISBN 9789811548277. - pp. 3-12 (( Intervento presentato al 5. convegno International Symposium on Signal Processing and Intelligent Recognition Systems tenutosi a Trivandrum nel 2019.
|Titolo:||Acoustic Identification of Nocturnal Bird Species|
NTALAMPIRAS, STAVROS (Corresponding)
|Parole Chiave:||Bird vocalizations; bioacoustics; acoustic ecology; machine learning; audio signal processing; audio pattern recognition|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-981-15-4828-4_1|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|