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.

  • Abstract

    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

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      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

    Received date: 2020-05-29

    Accepted date: 2020-06-08

    Published date: 2020-07-01