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Extraction and Analysis of Voice Samples Based on Short Audio Files

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Information and Software Technologies (ICIST 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 756))

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Abstract

Some voice defects may be removed from a recording in such a way that the remainder of the sample contains only the relevant data. However processing of input sounds to achieve this goal requires methodology that will be able to distinguish between clear sound and noise. In this paper, we propose data extraction technique and defect analysis. Developed methodology may allow for possible use in various life support systems, where sounds are processed to verify identity, control, communicate, etc. Proposed method has been tested and compared to other indirect methods.

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References

  1. Aggarwal, J., Ryoo, M.: Human activity analysis: a review. ACM Comput. Surv. 43, 1–43 (2011)

    Article  Google Scholar 

  2. Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer vision (1999)

    Google Scholar 

  3. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi:10.1007/11744023_32

    Chapter  Google Scholar 

  4. Nosáľ, M., Porubän, J., Sulír, M.: Customizing host IDE for non-programming users of pure embedded DSLs: a case study. Comput. Lang. Syst. Struct. 49, 101–118 (2017). doi:10.1016/j.cl.2017.04.003

    Google Scholar 

  5. Nosál, M., Sulír, M., Juhár, J.: Language composition using source code annotations. Comput. Sci. Inf. Syst. 13(3), 707–729 (2016). doi:10.2298/CSIS160114024N

    Article  Google Scholar 

  6. Chodarev, S.: Development of human-friendly notation for XML-based languages. In: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1565–1571. IEEE (2016). doi:10.15439/2016F530

  7. Marszałek, Z.: Performance test on triple heap sort algorithm. Tech. Sci. 20(1), 49–61 (2017)

    Google Scholar 

  8. Kapočiūtė-Dzikienė, J., Krilavičius, T.: Topic classification problem solving for morphologically complex languages. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 511–524. Springer, Cham (2016). doi:10.1007/978-3-319-46254-7_41

    Chapter  Google Scholar 

  9. Gabryel, M.: A bag-of-features algorithm for applications using a NoSQL database. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 332–343. Springer, Cham (2016). doi:10.1007/978-3-319-46254-7_26

    Chapter  Google Scholar 

  10. Gabryel, M.: The bag-of-features algorithm for practical applications using the MySQL database. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 635–646. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_56

    Google Scholar 

  11. Marszałek, Z.: Novel recursive fast sort algorithm. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 344–355. Springer, Cham (2016). doi:10.1007/978-3-319-46254-7_27

    Chapter  Google Scholar 

  12. Birvinskas, D., Jusas, V., Martisius, I., Damasevicius, R.: Fast DCT algorithms for EEG data compression in embedded systems. Comput. Sci. Inf. Syst. 12(1), 49–62 (2015). doi:10.2298/CSIS140101083B

    Article  Google Scholar 

  13. Mandziuk, J., Zychowski, A.: A memetic approach to vehicle routing problem with dynamic requests. Appl. Soft Comput. 48, 522–534 (2016). doi:10.1016/j.asoc.2016.06.032

    Article  Google Scholar 

  14. Swiechowski, M., Mandziuk, J.: Fast interpreter for logical reasoning in general game playing. J. Log. Comput. 26(5), 1697–1727 (2016). doi:10.1093/logcom/exu058

    Article  MathSciNet  MATH  Google Scholar 

  15. Artiemjew, P., Nowak, B.A., Polkowski, L.T.: A new classifier based on the dual indiscernibility matrix. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 380–391. Springer, Cham (2016). doi:10.1007/978-3-319-46254-7_30

    Chapter  Google Scholar 

  16. Artiemjew, P.: The boosting and bootstrap ensemble for classifiers based on weak rough inclusions. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS, vol. 9437, pp. 267–277. Springer, Cham (2015). doi:10.1007/978-3-319-25783-9_24

    Chapter  Google Scholar 

  17. Nowicki, R.K., Scherer, R., Rutkowski, L.: Novel rough neural network for classification with missing data. In: 21st International Conference on Methods and Models in Automation and Robotics MMAR 2016, Miedzyzdroje, Poland, 29 August–1 September 2016, pp. 820–825 (2016) doi:10.1109/MMAR.2016.7575243

  18. Wlodarczyk-Sielicka, M.: importance of neighborhood parameters during clustering of bathymetric data using neural network. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 441–452. Springer, Cham (2016). doi:10.1007/978-3-319-46254-7_35

    Chapter  Google Scholar 

  19. Brociek, R., Slota, D.: Application and comparison of intelligent algorithms to solve the fractional heat conduction inverse problem. Inf. Tech. Control 45(2), 184–194 (2016). doi:10.5755/j01.itc.45.2.13716

    Google Scholar 

  20. Jaworski, M., Duda, P., Rutkowski, L., Najgebauer, P., Pawlak, M.: Heuristic regression function estimation methods for data streams with concept drift. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS, vol. 10246, pp. 726–737. Springer, Cham (2017). doi:10.1007/978-3-319-59060-8_65

    Chapter  Google Scholar 

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Acknowledgments

Authors acknowledge contribution to this project to the Diamond Grant 2016 No. 0080/DIA/2016/45 funded by the Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100, Gliwice, Poland

    Dawid Połap & Marcin Woźniak

Authors
  1. Dawid Połap
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  2. Marcin Woźniak
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Correspondence to Marcin Woźniak .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Vilma Mikašytė

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Połap, D., Woźniak, M. (2017). Extraction and Analysis of Voice Samples Based on Short Audio Files. In: Damaševičius, R., Mikašytė, V. (eds) Information and Software Technologies. ICIST 2017. Communications in Computer and Information Science, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-67642-5_35

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  • DOI: https://doi.org/10.1007/978-3-319-67642-5_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67641-8

  • Online ISBN: 978-3-319-67642-5

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