Filip Korzeniowski, Gerhard Widmer,
"End-to-End Musical Key Estimation Using a Convolutional Neural Network"
: Proceedings of the 25th European Signal Processing Conference (EUSIPCO), 8-2017
End-to-End Musical Key Estimation Using a Convolutional Neural Network
Sprache des Titels:
Proceedings of the 25th European Signal Processing Conference (EUSIPCO)
We present an end-to-end system for musical key
estimation, based on a convolutional neural network. The proposed
system not only out-performs existing key estimation
methods proposed in the academic literature; it is also capable of
learning a unified model for diverse musical genres that performs
comparably to existing systems specialised for specific genres.
Our experiments confirm that different genres do differ in their
interpretation of tonality, and thus a system tuned e.g. for pop
music performs subpar on pieces of electronic music. They also
reveal that such cross-genre setups evoke specific types of error
(predicting the relative or parallel minor). However, using the
data-driven approach proposed in this paper, we can train models
that deal with multiple musical styles adequately, and without
major losses in accuracy.