@inproceedings{91759a2f46c64b5888cb59dfae1562a0,
title = "Environmental sound classification using central auditory representations and deep neural networks",
abstract = "This study presents an effective environmental sound classification method that combines central auditory representations and deep neural networks. The central auditory representation characterizes the responses of sound in primary auditory cortex and is related directly to human auditory perception. Meanwhile, the deep neural network is composed by no less than two hidden layers and is good at detecting high-level invariant features associated with the objects. An environmental sound classification task with 11 distinct classes is used for evaluation, and the timbre features and deep neural networks are combined and used as a baseline. The results show that the proposed method outperforms the baseline, and the highest accuracy of 89.94% is achieved by a two-layer network with 300 hidden nodes each layer.",
author = "Kean Chen and Lixue Yang and Zhiming Sang",
year = "2016",
language = "英语",
series = "ICSV 2016 - 23rd International Congress on Sound and Vibration: From Ancient to Modern Acoustics",
publisher = "International Institute of Acoustics and Vibrations",
booktitle = "ICSV 2016 - 23rd International Congress on Sound and Vibration",
note = "23rd International Congress on Sound and Vibration, ICSV 2016 ; Conference date: 10-07-2016 Through 14-07-2016",
}