2020,Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework

DOI:10.1016/j.dsp.2020.102943
paper

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multi-spectrogram:

• log-Mel spectrogram (log-Mel)
  STFT spectra \(S(f,t)=\sum_{n=0}^{N-1}x_{t}[n]w[n]e^{-i2\pi n f/f_{s}}\)
  Mel frequency warping \(f_{mel}=2595 log(1+f/700)\)
  simulates the overall frequency selectivity of the human auditory system
• Gammatonegram (Gamma)
  STFT spectra
  Gammatone weighting by \(g(t)=t^{P-1}e^{-2bt\pi}cos(2ft\pi+\theta)\)
  model the frequency-selective cochlea activation response of the human inner ear
• Constant Q Transform (CQT)
  model the geometric relationship of pitch,
  which makes it likely to be effective when undertaking a comparison between natural and artificial sounds, as well as being suitable for frequencies that span several octaves