Acoustic Event Classification using Graph Signals

被引：0
|
作者：
Mulimani, Manjunath [1 ]
Jahnavi, U. P. [2 ]
Koolagudi, Shashidhar G. [1 ]
机构：
[1] Natl Inst Technol Karnataka, Dept CSE, Surathkal 575025, India
[2] Gitam Inst Technol, Dept CSE, Visakhapatnam 530045, Andhra Prades, India
来源：
TENCON 2017 - 2017 IEEE REGION 10 CONFERENCE | 2017年
关键词：
AEC; spectrogram features; Time-Frequenc (TF) Representations (TFRs); graph signals; RECOGNITION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, a graph signal is generated from spectrogram and features are investigated from graph signal for Acoustic Event Classification (AEC). Different acoustic events are selected from Sound Scene Database of Real Word Computing Partnership (RWCP) group. Three different noises are selected from NOISEX'92 database and added to test samples at different noise conditions separately. The recognition performance of acoustic events using proposed features and Mel-frequency cepstral coefficients (MFCCs) with clean and noisy test samples are compared. The proposed features show significantly improved recognition accuracy over MFCCs in noisy conditions.
引用
收藏
页码：1812 / 1816
页数：5
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