Audio noise detection using hidden Markov model

Author

Sabri, Mahdi ; Alirezaie, Jmad ; Krishnan, Sridhar

fYear

2003

fDate

28 Sept.-1 Oct. 2003

Firstpage

637

Lastpage

640

Abstract

Simple noise level monitoring systems, which are currently used to create noise map in residential areas, are unable to identify source of environmental noise. The proposed automatic noise recognition (ANR) system can be used in conjunction with simple noise level monitoring to create an intelligent noise monitoring system (INMS). The presented system which is focused on aircraft noise detection, consists of two parts: feature extractor and training-recognition. We append linear prediction coefficients to Cepstrum coefficients to make a rich feature extractor. The hidden Markov model (HMM) is used for training and recognition. The required observation sequence is obtained by means of a vector quantization method based on fuzzy C-mean clustering. 15 signals are used for training and 28 signals are used in test phase. An overall 83% accuracy in classification is achieved.

Keywords

acoustic signal detection; feature extraction; fuzzy set theory; hidden Markov models; noise pollution; Cepstrum coefficients; aircraft noise detection; audio noise detection; automatic noise recognition; feature extractor; fuzzy C-mean clustering; hidden Markov model; intelligent noise monitoring system; linear prediction coefficients; noise map; training-recognition; Aircraft; Cepstrum; Computerized monitoring; Feature extraction; Hidden Markov models; Intelligent systems; Noise level; Testing; Vector quantization; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2003 IEEE Workshop on

Print_ISBN

0-7803-7997-7

Type

conf

DOI

10.1109/SSP.2003.1289568

Filename

1289568