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 [10.1007/978-981-15-4828-4_1].
Acoustic Identification of Nocturnal Bird Species
S. Ntalampiras
2020
Abstract
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%.File | Dimensione | Formato | |
---|---|---|---|
48 SIRS2019final.pdf
accesso riservato
Tipologia:
Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione
3.51 MB
Formato
Adobe PDF
|
3.51 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.