Filip Korzeniowski, Gerhard Widmer,
"On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition"
: Proceedings of the AES International Conference on Semantic Audio, 6-2017
Original Titel:
On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the AES International Conference on Semantic Audio
Original Kurzfassung:
Chord recognition systems use temporal models to
post-process frame-wise chord predictions from acoustic models. Traditionally, first-order models such
as Hidden Markov Models were used for this task, with recent works suggesting to apply Recurrent Neural Networks instead. Due to their ability to learn
longer-term dependencies, these models are supposed to learn and to apply musical knowledge, instead of
just smoothing the output of the acoustic model. In this paper, we argue that learning complex temporal
models at the level of audio frames is futile on principle, and that non-Markovian models do not perform
better than their first-order counterparts. We support our argument through three experiments on the
McGill Billboard dataset. The first two show 1) that when learning complex temporal models at the frame
level, improvements in chord sequence modelling are marginal; and 2) that these improvements do not
translate when applied within a full chord recognition system. The third, still rather preliminary experiment
gives first indications that the use of complex sequential models for chord prediction at higher temporal
levels might be more promising.
Forschungs-