Title :
New parameters for respiratory sound classification
Author :
Bahoura, Mohammed ; Pelletier, Charles
Author_Institution :
DMIG, Univ. du Quebec a Rimouski, Que., Canada
Abstract :
In this paper, a new approach based on cepstral analysis is proposed to classify respiratory sounds. The sound signal is divided into segments, which are characterized by a reduced number of cepstral coefficients. Those segments are then classified as whether containing wheezes or normal respiratory sounds, by using the vector quantization (VQ) method. This approach is tested and compared to other kind of features extraction like the autoregressive representation and the wavelet transform.
Keywords :
acoustic signal processing; cepstral analysis; feature extraction; signal classification; speech; vector quantisation; cepstral analysis; cepstral coefficient; features extraction; respiratory sound classification; signal segmentation; sound signal; vector quantization method; Cepstral analysis; Continuous wavelet transforms; Feature extraction; Frequency domain analysis; Neural networks; Signal analysis; Speech analysis; Testing; Vector quantization; Wavelet transforms;
Conference_Titel :
Electrical and Computer Engineering, 2003. IEEE CCECE 2003. Canadian Conference on
Print_ISBN :
0-7803-7781-8
DOI :
10.1109/CCECE.2003.1226178
