Sebastian Böck, Markus Schedl,
"Enhanced Beat Tracking with Context-Aware Neural Networks."
: Proceedings of the 14th International Conference on Digital Audio Effects (DAFx 2001), Paris, France., 2011
Enhanced Beat Tracking with Context-Aware Neural Networks.
Sprache des Titels:
Proceedings of the 14th International Conference on Digital Audio Effects (DAFx 2001), Paris, France.
We present two new beat tracking algorithms based on the autocorrelation
analysis, which showed state-of-the-art performance in
the MIREX 2010 beat tracking contest. Unlike the traditional approach
of processing a list of onsets, we propose to use a bidirectional
Long Short-Term Memory recurrent neural network to perform
a frame by frame beat classification of the signal. As inputs
to the network the spectral features of the audio signal and their
relative differences are used. The network transforms the signal
directly into a beat activation function. An autocorrelation function
is then used to determine the predominant tempo to eliminate
the erroneously detected - or complement the missing - beats. The
first algorithm is tuned for music with constant tempo, whereas
the second algorithm is further capable to follow changes in tempo
and time signature.