  In audio classification, differentiable auditory filterbanks with few
parameters cover the middle ground between hard-coded spectrograms and raw
audio. LEAF (arXiv:2101.08596), a Gabor-based filterbank combined with
Per-Channel Energy Normalization (PCEN), has shown promising results, but is
computationally expensive. With inhomogeneous convolution kernel sizes and
strides, and by replacing PCEN with better parallelizable operations, we can
reach similar results more efficiently. In experiments on six audio
classification tasks, our frontend matches the accuracy of LEAF at 3% of the
cost, but both fail to consistently outperform a fixed mel filterbank. The
quest for learnable audio frontends is not solved.
