Animal Sound Classification Using A Convolutional Neural Network

被引：0
|
作者：
Sasmaz, Emre [1 ]
Tek, F. Boray [1 ]
机构：
[1] Isik Univ, Dept Comp Engn, Istanbul, Turkey
来源：
2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK) | 2018年
关键词：
Animal sound classification; Mel Frequency Cepstral Coefficient (MFCC); Convolution Neural Network (CNN); Confusion Matrix (CF);
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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).
引用
收藏
页码：625 / 629
页数：5
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