  In recent years, filterbank learning has become an increasingly popular
strategy for various audio-related machine learning tasks. This is partly due
to its ability to discover task-specific audio characteristics which can be
leveraged in downstream processing. It is also a natural extension of the
nearly ubiquitous deep learning methods employed to tackle a diverse array of
audio applications. In this work, several variations of a frontend filterbank
learning module are investigated for piano transcription, a challenging
low-level music information retrieval task. We build upon a standard piano
transcription model, modifying only the feature extraction stage. The
filterbank module is designed such that its complex filters are unconstrained
1D convolutional kernels with long receptive fields. Additional variations
employ the Hilbert transform to render the filters intrinsically analytic and
apply variational dropout to promote filterbank sparsity. Transcription results
are compared across all experiments, and we offer visualization and analysis of
the filterbanks.
