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Convolutional Neural Networks for Scops Owl Sound Classification.
- Source :
- Procedia Computer Science; 2020, Vol. 179, p81-87, 7p
- Publication Year :
- 2020
-
Abstract
- Adopting a deep learning model into bird sound classification tasks becomes a common practice in order to construct a robust automated bird sound detection system. In this paper, we employ a four-layer Convolutional Neural Network (CNN) formulated to classify different species of Indonesia scops owls based on their vocal sounds. Two widely used representations of an acoustic signal: log-scaled mel-spectrogram and Mel Frequency Cepstral Coefficient (MFCC) are extracted from each sound file and fed into the network separately to compare the model performance with different inputs. A more complex CNN that can simultaneously process the two extracted acoustic representations is proposed to provide a direct comparison with the baseline model. The dual-input network is the well-performing model in our experiment that achieves 97.55% Mean Average Precision (MAP). Meanwhile, the baseline model achieves a MAP score of 94.36% for the mel-spectrogram input and 96.08% for the MFCC input. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18770509
- Volume :
- 179
- Database :
- Supplemental Index
- Journal :
- Procedia Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 148863351
- Full Text :
- https://doi.org/10.1016/j.procs.2020.12.010