International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Towards Low Complexity Model for Audio Event Detection

  • Saleem, Muhammad (Department of Telecommunication Engineering, Mehran University of Engineering & Technology) ;
  • Shah, Syed Muhammad Shehram (Department of Software Engineering, Mehran University of Engineering & Technology) ;
  • Saba, Erum (Information Technology Centre, Sindh Agriculture University) ;
  • Pirzada, Nasrullah (Department of Telecommunication Engineering, Mehran University of Engineering & Technology) ;
  • Ahmed, Masood (Department of Computer System, Mehran University of Engineering & Technology)
  • Received : 2022.09.05
  • Published : 2022.09.30
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Abstract

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.

Keywords

References

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