Sebastian Böck, Florian Krebs, Gerhard Widmer,
"Accurate Tempo Estimation based on Recurrent Neural Networks and Resonating Comb Filters"
: Proceedings of the 16th International Society for Music Information Retrieval Conference, 10-2015
Original Titel:
Accurate Tempo Estimation based on Recurrent Neural Networks and Resonating Comb Filters
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 16th International Society for Music Information Retrieval Conference
Original Kurzfassung:
In this paper we present a new tempo estimation algorithm
which uses a bank of resonating comb filters to determine
the dominant periodicity of a musical excerpt. Unlike existing
(comb filter based) approaches, we do not use handcrafted
features derived from the audio signal, but rather let
a recurrent neural network learn an intermediate beat-level
representation of the signal and use this information as input
to the comb filter bank. While most approaches apply
complex post-processing to the output of the comb filter
bank like tracking multiple time scales, processing different
accent bands, modelling metrical relations, categorising
the excerpts into slow/ fast or any other advanced processing,
we achieve state-of-the-art performance on nine
of ten datasets by simply reporting the highest resonator?s
histogram peak.