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Classification of human cough signals using spectro-temporal Gabor filterbank features

: Schröder, Jens; Anemüller, Jörn; Goetze, Stefan


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016. Proceedings : March 20-25, 2016, Shanghai International Convention Center, Shanghai, China
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-4799-9988-0 (electronic)
ISBN: 978-1-4799-9987-3 (USB)
ISBN: 978-1-4799-9989-7 (print)
International Conference on Acoustics, Speech and Signal Processing (ICASSP) <2016, Shanghai>
Fraunhofer IDMT ()

This contribution investigates the use of features derived from a Gabor filterbank (GFB) for the application of acoustic cough classification. Gabor filters are two-dimensional filters that decompose the spectro-temporal power density further into components which capture spectral, temporal and joint spectro-temporal modulation patterns. The proposed GFB feature extraction scheme in combination with Gaussian mixture model (GMM) and hidden Markov model (HMM) classifier back-ends is evaluated using a cough database recorded by a phone hotline. The database is composed of two kind of coughs, i.e., dry and productive cough, and other sounds, e.g. speech. Based on these data, we show that GFB features result in better recognition performance than the common Mel-frequency cepstral coefficient (MFCC) baseline for the given task of cough classification. Furthermore, results indicate that GMMs are preferable to HMMs for this kind of data.