Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks

被引：0
|
作者：
ZHANG Pengyuan [1 ,2 ]
CHEN Hangting [1 ,2 ]
BAI Haichuan [1 ,2 ]
YUAN Qingsheng [3 ]
机构：
[1] Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences
[2] University of Chinese Academy of Sciences
[3] National Computer Network Emergency Response Technical Team/Coordination Center of China
来源：
Chinese Journal of Electronics | 2019年 / 28卷 / 06期
关键词：
Acoustic scene classification; Time-delay neural network; Deep scattering spectrum; Detection and classification of acoustic scenes and events(DCASE);
D O I：
暂无
中图分类号：
TB52 [声学测量]; O657.3 [光化学分析法（光谱分析法）]; TP183 [人工神经网络与计算];
学科分类号：
070302 ; 0804 ; 081104 ; 0812 ; 081704 ; 0835 ; 1405 ;
摘要：
As one of the most commonly used features, Mel-frequency cepstral coefficients(MFCCs) are less discriminative at high frequency. A novel technique,known as Deep scattering spectrum(DSS), addresses this issue and looks to preserve greater details. DSS feature has shown promise both on classification and recognition tasks. In this paper, we extend the use of DSS feature for acoustic scene classification task. Results on Detection and classification of acoustic scenes and events(DCASE) 2016 and 2017 show that DSS provided 4.8% and 17.4% relative improvements in accuracy over MFCC features, within a state-of-the-art time delay neural network framework.
引用
收藏
页码：1177 / 1183
页数：7
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