Spectrogram-Based Classification Of Spoken Foul Language Using Deep CNN

@article{Wazir2020SpectrogramBasedCO,
  title={Spectrogram-Based Classification Of Spoken Foul Language Using Deep CNN},
  author={Abdulaziz Saleh Ba Wazir and Hezerul Abdul Karim and Mohd Haris Lye Abdullah and Sarina Mansor and Nouar Aldahoul and Mohammad Faizal Ahmad Fauzi and John See},
  journal={2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)},
  year={2020},
  pages={1-6}
}
  • A. Wazir, H. A. Karim, John See
  • Published 21 September 2020
  • Computer Science
  • 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)
Excessive content of profanity in audio and video files has proven to shape one’s character and behavior. Currently, conventional methods of manual detection and censorship are being used. Manual censorship method is time consuming and prone to misdetection of foul language. This paper proposed an intelligent model for foul language censorship through automated and robust detection by deep convolutional neural networks (CNNs). A dataset of foul language was collected and processed for the… 

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