Performance Analysis of Feature Extraction Methods in Indoor Sound Classification

被引：0

作者：

Calik, Nurullah ^{[1
]}

Durak Ata, Lutfiye ^{[1
]}

Serbes, Ahmet ^{[1
]}

Bolat, Bulent ^{[1
]}

Yavuz, Emrah ^{[2
]}

机构：

[1] Yildiz Tekn Univ, Elekt & Haberlesme Muhendisligi Bolumu, Istanbul, Turkey

[2] Arcelik AS, Uzman Ar Ge Muhendisi, Istanbul, Turkey

来源：

2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2015年

关键词：

MFCC; LPC; home enviroment sound; classification; warning sound;

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, by using a novel database of home enviroment warning sounds, the classification and recognition performances of these sounds are compared over feature extraction algorithms. Following the sample reduction of the feature vectors by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), k-Nearest Neighbour (k-NN) algorithm is employed for classification. Besides, a modified version of the algorithm for MF coefficients is proposed and we observe that the classification performance is better than MFCC and LPC even at low SNR values.

引用

页码：2025 / 2028

页数：4

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