Pathological voice detection based on gammatone short time spectral self-similarity_Journal of Biomedical Engineering

Authors：

ZHAO Denghuang , ZHOU Changwei , ZHU Xincheng ,  ZHANG Xiaojun ,  TAO Zhi

School of Optoelectronic Science and Engineering, Soochow University, Suzhou, Jiangsu 215000, P. R. China;

Corresponding author：

ZHANG Xiaojun, Email: zhangxj@suda.edu.cn; TAO Zhi, Email: taoz@suda.edu.cn

Keywords：

Pathological voice recognition; Chaos; Gammatone filter banks; Short time spectral self-similarity

DOI：

10.7507/1001-5515.202107037

Video：

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Abstract Full text Figures/Tables Video References Cited by

The acoustic detection method based on machine learning and signal processing is an important method of pathological voice detection and the extraction of voice features is one of the most important. Currently, the features widely used have disadvantage of dependence on the fundamental frequency extraction, being easily affected by noise and high computational complexity. In view of these shortcomings, a new method of pathological voice detection based on multi-band analysis and chaotic analysis is proposed. The gammatone filter bank was used to simulate the human ear auditory characteristics to analyze different frequency bands and obtain the signals in different frequency bands. According to the characteristics that turbulence noise caused by chaos in voice will worsen the spectrum convergence, we applied short time Fourier transform to each frequency band of the voice signal, then the feature gammatone short time spectral self-similarity (GSTS) was extracted, and the chaos degree of each band signal was analyzed to distinguish normal and pathological voice. The experimental results showed that combined with traditional machine learning methods, GSTS reached the accuracy of 99.50% in the pathological voice database of Massachusetts Eye and Ear Infirmary (MEEI) and had an improvement of 3.46% compared with the best existing features. Also, the time of the extraction of GSTS was far less than that of traditional nonlinear features. These results show that GSTS has higher extraction efficiency and better recognition effect than the existing features.

Citation： ZHAO Denghuang, ZHOU Changwei, ZHU Xincheng, ZHANG Xiaojun, TAO Zhi. Pathological voice detection based on gammatone short time spectral self-similarity. Journal of Biomedical Engineering, 2022, 39(4): 694-701, 712. doi: 10.7507/1001-5515.202107037 Copy

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13.	Zhao X. CASA-based robust speaker identification. Ohio: The Ohio State University, 2014.
14.	Gomez J A, Moro L, Godino J I. On the design of automatic voice condition analysis systems. Part I: Review of concepts and an insight to the state of the art. Biomed Signal Process Control, 2019, 51: 181-199.
15.	Jiang J J, Yu Z, Stern J. Modeling of chaotic vibrations in symmetric vocal folds. J Acoust Soc Am, 2001, 110(4): 2120-2128.
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17.	Dibazar A A, Park H O, Berger T W. Nonlinear dynamic modeling of impaired voice// 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Jeju-do: IEEE, 2010: 2770-2773.
18.	DaJer M E, Pereira J C, Maciel C D. Nonlinear dynamical analysis of normal voices// IEEE International Symposium on Multimedia. San Diego: IEEE, 2006: 1-6.
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20.	陈亮, 张雄伟. 语音信号非线性特征的研究. 解放军理工大学学报: 自然科学版, 2000(2): 11-17.
21.	Takens F. Detecting strange attractors in turbulence// Rand D, Young L S. Dynamical Systems and Turbulence, Warwick 1980. Lecture Notes in Mathematics. Berlin, Heidelberg: Springer, 1981, 898: 366-381.
22.	Lin L, Calawerts W, Dodd K, et al. An objective parameter for quantifying the turbulent noise portion of voice signals. J Voice, 2016, 30(6): 664-669.
23.	李佳芮, 洪缨. 喘鸣音的声谱图熵特征分析及检测. 声学学报, 2020, 45(1): 131-136.
24.	Zhou C W, Zhang L L, Tao Z, et al. Classification of normal and pathological voices using convolutional neural network// 2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). Xi’an: IEEE, 2020: 325-329.

1. Liu B, Polce E, Sprott J C, et al. Applied chaos level test for validation of signal conditions underlying optimal performance of voice classification methods. J Speech Lang Hear Res, 2018, 16(5): 1130-1139.
2. 彭策, 万柏坤. 嗓音分析在疾病诊断中的应用. 生物医学工程学杂志, 2007, 24(6): 1419-1422.
3. Virgilijus U, Evaldas P, Aurelija V, et al. Exploring the feasibility of smart phone microphone for measurement of acoustic voice parameters and voice pathology screening. Eur Arch Otorhinolaryngol, 2015, 272(11): 3391-3399.
4. Hua S C, Ding J J, Wang C H, et al. Learning and feature extraction based fundamental frequency determination algorithm in very low SNR scenario// 2020 IEEE International Symposium on Circuits and Systems (ISCAS). Seville: IEEE, 2020: 1-5.
5. Zhang T, Wu Y, Shao Y, et al. A pathological multi-vowels recognition algorithm based on LSP feature. IEEE Access, 2019, 7: 58866-58875.
6. Wu Y, Zhou C, Fan Z, et al. Voice pathology detection and multi-classification using machine learning classifiers// 2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). Xi'an: IEEE, 2020: 319-324.
7. 甘德英, 胡维平, 赵冰心. 传统声学特征和非线性特征用于病态嗓音的比较研究. 生物医学工程学杂志. 2014, 31(5): 1149-1154.
8. Jiang J J, Zhang Y, Mcgilligan C. Chaos in voice, from modeling to measurement. J Voice, 2006, 20(1): 2-17.
9. Arias J D, Godino J I, Sáenz N, et al. Automatic detection of pathological voices using complexity measures, noise parameters, and Mel-cepstral coefficients. IEEE T Bio-Med Eng, 2011, 58(2): 370-379.
10. 周强, 张晓俊, 陶智, 等. 嗓音多频带非线性分析的声带病变识别. 声学学报, 2014, 39(1): 111-118.
11. Liu B, Polce E, Jiang J J. An objective parameter to classify voice signals based on variation in energy distribution. J Voice, 2019, 33(5): 591-602.
12. Hu Zhangfang, Yue Congcong, Luo Yuan, et al. Research of the auditory feature extraction algorithm based on gammatone filter bank// 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC). Chongqing: IEEE, 2017: 444-449.
13. Zhao X. CASA-based robust speaker identification. Ohio: The Ohio State University, 2014.
14. Gomez J A, Moro L, Godino J I. On the design of automatic voice condition analysis systems. Part I: Review of concepts and an insight to the state of the art. Biomed Signal Process Control, 2019, 51: 181-199.
15. Jiang J J, Yu Z, Stern J. Modeling of chaotic vibrations in symmetric vocal folds. J Acoust Soc Am, 2001, 110(4): 2120-2128.
16. Jiang J J, Yu Z, Ford C N. Nonlinear dynamics of phonations in excised larynx experiments. J Acoust Soc Am, 2003, 114(4 Pt 1): 2198-2205.
17. Dibazar A A, Park H O, Berger T W. Nonlinear dynamic modeling of impaired voice// 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Jeju-do: IEEE, 2010: 2770-2773.
18. DaJer M E, Pereira J C, Maciel C D. Nonlinear dynamical analysis of normal voices// IEEE International Symposium on Multimedia. San Diego: IEEE, 2006: 1-6.
19. 陈莉媛, 薛隆基, 陶智, 等. 非对称黏性空气动力学声带模型及其病理喉声源分类. 声学学报, 2020, 45(5): 153-163.
20. 陈亮, 张雄伟. 语音信号非线性特征的研究. 解放军理工大学学报: 自然科学版, 2000(2): 11-17.
21. Takens F. Detecting strange attractors in turbulence// Rand D, Young L S. Dynamical Systems and Turbulence, Warwick 1980. Lecture Notes in Mathematics. Berlin, Heidelberg: Springer, 1981, 898: 366-381.
22. Lin L, Calawerts W, Dodd K, et al. An objective parameter for quantifying the turbulent noise portion of voice signals. J Voice, 2016, 30(6): 664-669.
23. 李佳芮, 洪缨. 喘鸣音的声谱图熵特征分析及检测. 声学学报, 2020, 45(1): 131-136.
24. Zhou C W, Zhang L L, Tao Z, et al. Classification of normal and pathological voices using convolutional neural network// 2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). Xi’an: IEEE, 2020: 325-329.

Journal of Biomedical Engineering

Pathological voice detection based on gammatone short time spectral self-similarity

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