Acoustic Scene Classification Using Joint Time-Frequency Image-Based Feature Representations

@article{Abidin2018AcousticSC,
  title={Acoustic Scene Classification Using Joint Time-Frequency Image-Based Feature Representations},
  author={Shamsiah Abidin and Roberto Togneri and Ferdous Sohel},
  journal={2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
  year={2018},
  pages={1-6}
}
The classification of acoustic scenes is important in emerging applications such as automatic audio surveillance, machine listening and multimedia content analysis. In this paper, we present an approach for acoustic scene classification by using joint time-frequency image-based feature representations. In acoustic scene classification, joint time-frequency representation (TFR) is shown to better represent important information across a wide range of low and middle frequencies in the audio… CONTINUE READING

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Key Quantitative Results

  • Our technique achieves a competitive performance with a classification accuracy of 83.4% on the DCASE 2016 development dataset compared to the existing current state of the art.

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