Acoustic data classification using random forest algorithm and feed forward neural network

  • Authors

    • Ali Najdet Nasret Coran cankaya university ankara
    • Prof Dr. Hayri Sever cankaya university
    • Dr. Murad Ahmed Mohammed Amin northern technical university
    2020-07-01
    https://doi.org/10.14419/ijet.v9i2.30815
  • FFNN, RF, MFCC, Pitch Period, Sampling Rate, Neurons, Weight.

  • Speaker identification systems are designed to recognize the speaker or set of speakers according to their acoustic analysis. Many approach-es are made to perform the acoustic analysis in the speech signal, the general description of those systems is time and frequency domain analysis. In this paper, acoustic information is extracted from the speech signals using MFCC and Fundamental Frequency methods combi-nation. The results are classified using two different algorithms such as Random-forest and Feed Forward Neural Network. The FFNN classifier integration with the acoustic model resulted a recognition accuracy of 91.4 %. The CMU ARCTIC Database is referred in this work.

     

     

  • References

    1. [1] ITU Radio Regulations – Article 1, Definitions of Radio Services, Article 1.2 Administration: Any governmental department or ser- vice responsible for discharging the obligations undertaken in the Constitution of the International Telecommunication Union, in the Convention of the International Telecommunication Union and in the Administrative Regulations (CS1002).

      [2] International Telecommunication Union´s Radio Regulations, Edi- tion of2012.

      [3] Colin Robinson (2003). Competition and regulation in utility mar- kets. Edward Elgar Publishing. p. 175. ISBN978-1-84376-230-0.

      [4] AyubiPreet, Department of Computer Science and Engineering, SGGSWU, Fatehgarh Sahib, Punjab, India (140406), AyubiPreet et al, / (IJCSIT) International Journal of Computer Science and Infor- mation Technologies, Vol. 5 (4), 2014,5508-5511.

      [5] ParthaPratim Bhattacharya, RonakKhandelwal, Rishita Gera, Anjali Agarwal,†Smart Radio Spectrum Management for Cognitive Ra- dioâ€, Department of Electronics and Communication Engineering Faculty of Engineering and Technology Mody Institute of Technol- ogy and Science (Deemed University), International Journal of Distributed and Parallel Systems (IJDPS) Vol.2, No.4, July2011.

      [6] Bablu Kumar Singh, JitendraJangir, “A Study of Recent Trends in Cognitive Radio Communications and Networks for Licence Free Connectivityâ€, International Journal of Engineering Research & Technology (IJERT) ISSN:2278-0181.

      [7] GoutamGhosh, Prasun Das and Subhajit Chatterjee, “Cognitive Ra- dio and Dynamic Spectrum Access a Studyâ€, International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March2014. https://doi.org/10.5121/ijngn.2014.6104.

      Mahmood, Zuhair Shakor, Ali Najdet Nasret Coran, and Attallah Younus Aewayd. "The Impact of Relay Node Deployment In Vehicle Ad Hoc Network: Reachability Enhancement Approach." 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019. https://doi.org/10.1109/GCAT47503.2019.8978445

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  • How to Cite

    Najdet Nasret Coran, A., Hayri Sever, P. D., & Murad Ahmed Mohammed Amin, D. (2020). Acoustic data classification using random forest algorithm and feed forward neural network. International Journal of Engineering & Technology, 9(2), 582-585. https://doi.org/10.14419/ijet.v9i2.30815