A comparison of audio features for elementary sound based audio classification

Author

Gubka, R. ; Kuba, M.

Author_Institution

Dept. of Telecommun. & Multimedia, Univ. of Zilina, Zilina, Slovakia

fYear

2013

fDate

29-31 May 2013

Firstpage

14

Lastpage

17

Abstract

In this paper we compare two sets of audio features in task of audio pattern searching based on elementary sound models. The rst set of features consist of well-known mel-frequency cepstral coefficients together with their rst and second order time derivatives. The second set was chosen from bag of features by particle swarm optimization algorithm and consist of following audio features: line spectral frequencies (LSF), spectral ux (SFX) and zero crossing rate (ZCR). Experimental results performed on AudioDat sound database show improvement of above 18.6 % of average F-measure when using the second selected combination of features.

Keywords

audio signal processing; cepstral analysis; particle swarm optimisation; signal classification; AudioDat sound database show improvement; LSF; SFX; ZCR; audio feature comparison; audio pattern searching; elementary sound based audio classification; first order time derivative; frequency cepstral coefficient; line spectral frequency; particle swarm optimization algorithm; second order time derivative; zero crossing rate; Computational modeling; Decoding; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Speech; Vectors; audio features; elementary sounds; pattern modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Technologies (DT), 2013 International Conference on

Conference_Location

Zilina

Print_ISBN

978-1-4799-0923-0

Type

conf

DOI

10.1109/DT.2013.6566278

Filename

6566278