Animal sound classification using a convolutional neural network
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Dosyalar
Tarih
2018-12-06
Yazarlar
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Dergi ISSN
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Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, we investigate the problem of animal sound classification using deep learning and propose a system based on convolutional neural network architecture. As the input to the network, sound files were preprocessed to extract Mel Frequency Cepstral Coefficients (MFCC) using LibROSA library. To train and test the system we have collected 875 animal sound samples from an online sound source site for 10 different animal types. We report classification confusion matrices and the results obtained by different gradient descent optimizers. The best accuracy of 75% was obtained by Nesterov-accelerated Adaptive Moment Estimation (Nadam).
Açıklama
Anahtar Kelimeler
Animal sound classification, Mel frequency cepstral coefficient (MFCC), Convolution neural network (CNN), Confusion matrix (CF), Birds, Acoustics, Acoustic indices, Convolution, Deep learning, Image resolution, Network architecture, Neural networks, Animal types, Confusion matrices, Convolution neural network, Convolutional neural network, Gradient descent, Mel-frequency cepstral coefficients, Moment estimation, Sound classification, Animals
Kaynak
2018 3rd International Conference on Computer Science and Engineering (UBMK)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Şaşmaz, E. & Tek, F. B. (2018). Animal sound classification using A convolutional neural network. Paper presented at the 2018 3rd International Conference on Computer Science and Engineering (UBMK), 625-629. doi:10.1109/UBMK.2018.8566449
