Consider incorporating other representations of sound into our model about audiotagging

[JoshVarty]

The window size for computing spectra trades temporal
resolution (short windows) against frequential resolution (long
windows). Both for log-mel and constant-Q spectra, it is
possible to use shorter windows for higher frequencies, but this
results in inhomogeneously blurred spectrograms unsuitable
for spatially local models. Alternatives include computing
spectra with different window lengths, projected down to the
same frequency bands, and treated as separate channels [16].
In [17] the authors also investigated combinations of different
spectral features.

[JoshVarty]

I think there are a few experiments here worth carrying out:

1. Create logmel spectrograms with 3 different window sizes (n_fft) and stack them together into RGB images
2. Try stacking other representations with logmel spectrogram. Constant Q Transform seems to be a popular one. Continuous Wavelet Transform is another. For more information see: https://arxiv.org/pdf/1706.07156.pdf

[JoshVarty]

xresnet18: 0.8409990
xresnet18: 0.8438536
xresnet18: 0.8428597
xresnet18 with 3 channels: 0.845929
xresnet18 with 3 channels: 0.844033
xresnet18 with 3 channels: 0.846719

Looks like there's a marginal increase about ~0.003