Discriminating Parkinson diseased and healthy people using modified MFCC filter bank approach
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In this paper a modified Mel scaled filter bank-based approach to discriminate people suffering from Parkinson disease (PD) in their early stages from healthy people using speech samples is proposed. Parkinson’s disease not only affects the muscular activities of the human body but also affects the speech of the diseased. So, the speech features of Parkinson affected people tend to vary and hence differ from the speech features of healthy people. In this paper, the speech feature used for discriminating the two groups is the Mel frequency cepstral coefficients (MFCC) extracted from speech samples of both the PD and healthy people. The traditional way of computing the MFCC coefficients involves the design of the Mel filter bank. These filters are usually designed according to the auditory or acoustic system of human ear which follows the Mel scale. In this study, modification to this Mel scaled bank of filters is done by varying its bandwidth in the region of interest to compute the feature, MFCC and its performance is then compared with the conventionally designed MFCC filter bank for the said application. The performance is compared in terms of classification accuracy using radial basis network classifier. The results show an improvement of 6.3% in the classification accuracy obtained using the proposed method.
KeywordsParkinson disease MFCC Filter bank Bandwidth Modified Mel filter bank
- Benba, A., Jilbab, A., Hammouch, A., & Sandabad, S. (2015). Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson’s disease. In IEEE 1st international conference on electrical and information technologies ICEIT’2015 (pp. 300–304).Google Scholar
- Do, M. N. (2016) An automatic speaker recognition system, Audio Visual Communications Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland. Retrieved May, 2016, from http://lcavwww.epfl.ch/~minhdo/asr_project/.
- Han, W., Chan, C. F., Choy, C. S., Pun, K. P. (2006). An efficient MFCC extraction method in speech recognition. In Proceedings of the IEEE international symposium on circuits and systems (ISCAS’2006) (pp. 145–148).Google Scholar
- Kopparapu, S & Narayana, L (2010). Choice of Mel filter bank in computing MFCC of a resampled speech. In International conference on information science, signal processing and their applications (ISSPA).Google Scholar
- Molau, S., Pitz, M., Schliitel, R., Ney, H. (2001). Computing Mel-frequency cepstral coefficients on the power spectrum”. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP’2001) (pp. 73–76).Google Scholar
- Okan Sakar, C., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., et al. (2019). A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing Journal,74, 255–263.CrossRefGoogle Scholar
- Oung, Q. W., Basah, S. N., Muthusamy, H., Vijean, V., Lee, H. (2017) Evaluation of short-term cepstral based features for detection of Parkinson’s Disease severity levels through speech signals. In MUCET 2017 IOP publishing IOP conference series: Materials science and engineering (p. 318).Google Scholar
- Retrieved December, 2018, from https://mccormickml.com/2013/08/15/radial-basis-function-network-rbfn-tutorial.
- Retrieved December, 2018, from https://www.saedsayad.com/artificial_neural_network_rbf.htm.
- Skowronski, M. & Harris, J. (2002). Increased MFCC filter bandwidth for noise-robust phoneme recognition. In IEEE international conference on acoustics, speech, and signal processing ICASSP 2002, (pp. 801–804).Google Scholar
- Skowronski, M. & Harris, J. (2003). Improving the filter bank of classic speech feature extraction algorithm. In Proceedings of the 2003 international symposiumon circuits and systems ISCAS 2003, (pp. 281–284).Google Scholar
- Vignolo, L. D., Rufiner, H. L., Milone, D. H. (2009). Genetic optimization of cepstrum filterbank for phoneme classification. In Proceedings of the second international conference on bio-inspired systems and signal processing (BIOSIGNALS 2009), pp. 179-185.Google Scholar
- Wrigley, S. N. (2015) Speech recognition by dynamic time warping, Speech and Hearing Research Group, University of Sheffield, Sheffield S1 4DP, United Kingdom. Retrieved March, 2015, from https://www.dcs.shef.ac.uk/~ stu/com326/sym.html.