@inproceedings{3045bdadf0044d04a1dc4e170cdf807c,
title = "Deep Convolutional Neural Network with Transfer Learning for Environmental Sound Classification",
abstract = "Environmental sound classification (ESC) is an important issue. However, due to the lack of datasets, high-Accuracy ESC has always been challenging. In this paper, we propose a new convolutional neural network (CNN) model using transfer learning technology for ESC task. First, we represent sound as RGB image, where the red channel corresponds to the Log-Mel spectrogram, the green channel corresponds to the scalogram, and the blue channel corresponds to the Mel frequency cepstrum coefficient (MFCC). Second, we train a CNN architecture based on Xception model which has a better performance on the JFT dataset. Test results show that the proposed approach is with a better performance on the ESC accuracy.",
keywords = "CNN, ESC-50, Log-Mel spectrogram, MFCC, Xception, environmental sound classification, scalogram, transfer learning",
author = "Jianrui Lu and Ruofei Ma and Gongliang Liu and Zhiliang Qin",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Computer, Control and Robotics, ICCCR 2021 ; Conference date: 08-01-2021 Through 10-01-2021",
year = "2021",
month = jan,
day = "8",
doi = "10.1109/ICCCR49711.2021.9349393",
language = "英语",
series = "2021 International Conference on Computer, Control and Robotics, ICCCR 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "242--245",
booktitle = "2021 International Conference on Computer, Control and Robotics, ICCCR 2021",
address = "美国",
}