  Extracting pitch information from music recordings is a challenging but
important problem in music signal processing. Frame-wise transcription or
multi-pitch estimation aims for detecting the simultaneous activity of pitches
in polyphonic music recordings and has recently seen major improvements thanks
to deep-learning techniques, with a variety of proposed network architectures.
In this paper, we realize different architectures based on CNNs, the U-net
structure, and self-attention components. We propose several modifications to
these architectures including self-attention modules for skip connections,
recurrent layers to replace the self-attention, and a multi-task strategy with
simultaneous prediction of the degree of polyphony. We compare variants of
these architectures in different sizes for multi-pitch estimation, focusing on
Western classical music beyond the piano-solo scenario using the MusicNet and
Schubert Winterreise datasets. Our experiments indicate that most architectures
yield competitive results and that larger model variants seem to be beneficial.
However, we find that these results substantially depend on randomization
effects and the particular choice of the training-test split, which questions
the claim of superiority for particular architectures given only small
improvements. We therefore investigate the influence of dataset splits in the
presence of several movements of a work cycle (cross-version evaluation) and
propose a best-practice splitting strategy for MusicNet, which weakens the
influence of individual test tracks and suppresses overfitting to specific
works and recording conditions. A final evaluation on a mixed dataset suggests
that improvements on one specific dataset do not necessarily generalize to
other scenarios, thus emphasizing the need for further high-quality multi-pitch
datasets in order to reliably measure progress in music transcription tasks.
