Performance analysis of feature extraction methods in indoor sound classification

Author

Calik, Nurullah ; Durak Ata, Lutfiye ; Serbes, Ahmet ; Bolat, Bulent ; Yavuz, Emrah

Author_Institution

Elektron. ve Haberlesme Muhendisligi Bolumu, Yildiz Teknik Univ., İstanbul, Turkey

fYear

2015

fDate

16-19 May 2015

Firstpage

2025

Lastpage

2028

Abstract

In this paper, by using a novel database of home environment 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.

Keywords

feature extraction; principal component analysis; signal classification; vectors; LDA; MF coefficients; PCA; feature extraction methods; feature vector reduction; home environment warning sounds; indoor sound classification; k-NN algorithm; k-nearest neighbour algorithm; linear discriminant analysis; low SNR values; performance analysis; principal component analysis; sound classification; sound recognition; Classification algorithms; Feature extraction; Hidden Markov models; IEEE Engineering in Medicine and Biology Society; Mel frequency cepstral coefficient; Principal component analysis; Signal to noise ratio; LPC; MFCC; classification; home environment sound; warning sound;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2015 23th

Conference_Location

Malatya

Type

conf

DOI

10.1109/SIU.2015.7130263

Filename

7130263