- Tytuł:
- Marine Mammals Classification using Acoustic Binary Patterns
- Autorzy:
-
Nadir, Maheen
Adnan, Syed M.
Aziz, Sumair
Khan, Muhammad Umar - Powiązania:
- https://bibliotekanauki.pl/articles/1953520.pdf
- Data publikacji:
- 2020
- Wydawca:
- Polska Akademia Nauk. Czasopisma i Monografie PAN
- Tematy:
-
marine mammals
1D Local Binary Patterns
Mel frequency cepstral coefficients
feature extraction
passive acoustic monitoring - Opis:
- Marine mammal identification and classification for passive acoustic monitoring remain a challenging task. Mainly the interspecific and intraspecific variations in calls within species and among different individuals of single species make it more challenging. Varieties of species along with geographical diversity induce more complications towards an accurate analysis of marine mammal classification using acoustic signatures. Prior methods for classification focused on spectral features which result in increasing bias for contour base classifiers in automatic detection algorithms. In this study, acoustic marine mammal classification is performed through the fusion of 1D Local Binary Pattern (1D-LBP) and Mel Frequency Cepstral Coefficient (MFCC) based features. Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. Classification of six species named Tursiops truncatus, Delphinus delphis, Peponocephala electra, Grampus griseus, Stenella longirostris, and Stenella attenuate are targeted in this research. The proposed model achieved 90.4% accuracy on 70-30% training testing and 89.6% on 5-fold cross-validation experiments.
- Źródło:
-
Archives of Acoustics; 2020, 45, 4; 721-731
0137-5075 - Pojawia się w:
- Archives of Acoustics
- Dostawca treści:
- Biblioteka Nauki
Artykuł