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Application of digital signal processing in biological sciences

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Almost all biological phenomena, physiological functions and psycho-physiological conditions can be more completely ana-lyzed, in details, by contemporary technologies and methods. Foremost among these is digital signal processing (DSP). DSP is one of the most powerful technologies that will shape sci-ence and engineering in the twenty-first century. It refers to methods of filtering, processing and analyzing signals based on the assumption that signal amplitudes can be represented by a finite set of integers corresponding to the amplitude of the

signal at a finite number of points in time (Mulgrew et al., 2002; Kuo and Gan, 2005). By definition, DSP is the study of signals in a digital representation and the processing methods of these signals. Also, DSP analyses audio and speech signal processing, video and digital image processing, signal processing, radar (and sonar) signal processing, spectral estimation, statistical signal processing, signal processing for communications, sen-sors and biomedical signal processing, seismic data processing, etc. (Blackledge and Turner, 2001). Basically, DSP is designated

APPLICATION OF DIGITAL SIGNAL PROCESSING IN BIOLOGICAL SCIENCES. Svetlana Čičević

1

,

Milkica Nešić

2

,

and

V. Vučković

3

.

1

Faculty of Transport and Traffic Engineering, University of Belgrade,

11000

Belgrade, Serbia;

2

Department of Physiology, Faculty of Medicine, University of Niš,

18000 Niš, Serbia

;

3

Faculty

of Electronic Engineering, University of Niš,

18000 Niš, Serbia.

UDc 576.32/.36

P

Arch. Biol. Sci., Belgrade, 61 (4), P-11P, 200 DOI:10.228/ABS004Pc

Key words: Digital signal processing (DSP), speech analysis, software application, fundamental frequency (F0)

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10P

F0. Speech analysis is the branch of speech science that estimates the parameters of the model for speech production from acoustic measurements of speech signal. One such very important feature is voice fundamental frequency (F0). The main parameter, fundamental frequency, origins from the bio-mechanical characteristics of human vocal tract and could be used as additional cognitive parameter for classification process. F0 tracking algorithms generally do not identify the temporal location of each vibration of the vocal chords but rather are based on average time spacing between numbers of vibrations. The averaging, typically due to the use of an autocorrelation type of calculation is used to improve the accuracy of tracking. F0 extraction plays an important role in speech processing and has a wide spread of applications. For this reason, many methods to extract the F0 of speech signals have been proposed. With these methods, the F0 of each short time speech frame is determined based on the periodicity or the harmonic structure of the frame, by detecting the time delay that maximizes the autocorrelation function of the frame, or by detecting the frequency whose multiples correspond to the harmonic frequency peaks in the frequency spectrum of the frame (Noll, 167; Schroeder, 168; Sondhi, 168; Rabiner et al., 176; Wise, 176). Recently, several new F0 estimation methods have been proposed for provid-ing better performance than traditional methods (Boersma, 13; Kawahara et al., 1; Liu and Lin, 2001; Shimamura and Kobayashi, 2001; De cheveigné and Kawahara, 2002). F0, if reli-ably estimated, could be useful for a wide range of applications, and has been successfully employed to examine the influence of stress, effort, fatigue and physical and mental load (Ruiz et al., 10; Brenner et al., 14).

Fig. 2.Variations of fundamental frequency of pronounced material (test word divided into segments) in three diferent functional states – rest, regular working situation, and in the state of stress.

to process continuous real-world analog signals, so the first step is to convert the signal from its analog form to a digital by using an analog to digital converter (ADc). Besides some other advanced technologies could be used like more powerful general-purpose RISc microprocessors; field-programmable gate arrays (FPGAs), digital signal controllers, and stream and systolic array processors. Likewise, an interesting application is the design of digital systems for arbitrary sampling rate conver-sion (Blackledge and Turner, 2001).

Sound signals. All living beings communicate among

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ADS v2.0. Employing the basic principles of digital signal

processing approach, the algorithm based upon a simplified version of a general technique for F0 extraction, has been implemented and incorporated in the speech analysis applica-tion ADS v2.0, created by the authors. The applicaapplica-tion ADS v2.0 was written with primary purpose to increase efficiency of the procedure, especially in filter phase and/or computing of the basic voice parameters (Vučković, 17). All component appli-cations are interactive, and are to be performed in real time. It is developed according to specific consumer needs and referred to medical and psycho-physiological stress assessment (Čičević et al., 200). It supports the standards in visualization of the digital signal using different functions for calculation of the standard parameters of speech (segment length, intensity, frequency, etc.) as well as supporting list of specialized function for concrete application in the projected area. The new idea is based on transforming one-dimensional sound quantity into 2-dimen-sional wave shapes and their classification by using standard pattern recognition procedures. The recognizing application is implemented, tested and proved the value of new approach.

Application. To increase system performances, data base

with referent samples is stored in the operative memory. DSP module has responsibility to filter input signal. After that, normalization and pick elimination procedures are performed. Recognizing procedure compares normalized input signal with all samples from referent samples base, and one sample, which is most similar to input one, represents the final result of the pro-cess. The parameter data file that is generated could be printed immediately. Nevertheless, its format is not suitable for statisti-cal analysis because it has too many details. The other reasons for further computing are the linkage to standard statistical applications. Those problems are solved with FILTER applica-tion. The user could select the parameters for filtering. Using that defaults, the application filters the original AScII prm.txt into format that is suitable for statistical analysis (usage of SPSS software, for instance).

In conclusion. Proposed software application showed

appli-cability for individuals` stress and emotional tension estimation

in real working situation. The results of the study on air traffic controllers indicated that F0 increased significantly after being exposed to stress in comparison to the resting state, as well as in subjects who worked under regular conditions. Such findings indicate that F0 shows significant sensitivity both to stress and working intervals of relatively long duration, when accumula-tion of workload takes place.

References: - Blackledge, J. M., and M. Turner (2001). Digital

Signal Processing: Mathematical and Computational Methods,

Software Development and Applications. Horwood Publishing

Ltd. - Boersma, P. (13). Proceedingsof the Institute of Phonetic

Sciences, University of Amsterdam17, 7-110. - Brenner, M.,

Doherty, E. T, and T. Doherty (14). Aviat. Space Envir. Med. 65, 21-26. - Čičević, S., Nešić, M., and V. Vučković (200).

Symopis, 381-385 - De cheveigné, A., and H. Kawahara (2002).

J. Acoust. Soc. Am. 111, 117-130. - Fitch, W. T. (17). J.

Acoust. Soc. Am.102, 1213-1222. - Fitch, W. T. (2000). Phonetica

57, 205-218. - Jeon, J. H. (200). Ital. J. Anim. Sci.8, 113-11. - Kawahara, H., Masuda-Katsuse, I., and A. De cheveigné (1).

Speech Commun.27, 187-207. - Kuo, S. M., and W. S. S.Gan

(2005). Digital Signal Processors: Architectures, Implementations,

and Applications.Prentice Hall. - Liu, D. J., and c. T. Lin (2001).

IEEE Trans. Speech Audio Process. 9, 60-621. - Mulgrew, B.,

Grant, P. M., and J. Thompson (2002). Digital Signal Processing

– Concepts and Applications. Palgrave Macmillan. - Noll, A.

M. (167). J. Acoust. Soc. Am.41 (2), 23-30. - Rabiner, L. R., cheng, M., Rosenberg, A., and c. McGonegal (176). IEEE

Trans. Audio Speech Lang. Process. 24, 3-418. - Ruiz, R.,

Legros, c., and A. Guell (10). Aviat. Space Environ. Med. 61, 266-271. - Shimamura, T., and H. Kobayashi (2001). IEEE Trans.

Speech Audio Process. 9, 727-730. - Schroeder, M. R. (168). J.

Acoust. Soc. Am. 43, 82-834. - Sondhi, M. M. (168). IEEE

Trans. Audi. Electroacoust. 16, 262-266. - Tokuda, I. (2002). J.

Acoust. Soc. Am.111, 208-21. - Vučković, V. (17). Master

Thesis, Faculty of Electronic Engineering, University of Niš.

- Wise, J. D., caprio, J. R., and T. W. Parks (176). IEEE Trans.

Imagem

Fig.  1 .  Comparisons  of  acoustical  diferences  between  categories  of  sound,  illustrating  similarities  between  tonal and human speech sounds relative to tool sounds; spectrogram of human speech and a bottlenose  dol-phin whistle.
Fig. 2 .  Variations of fundamental frequency of pronounced material (test word divided into segments) in  three diferent functional states – rest, regular working situation, and in the state of stress.

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