Jan Schlüter, Sebastian Böck,
"Improved Musical Onset Detection with Convolutional Neural Networks"
: Proceedings of the 39th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 5-2014
Improved Musical Onset Detection with Convolutional Neural Networks
Sprache des Titels:
Proceedings of the 39th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Musical onset detection is one of the most elementary tasks in music analysis, but still only solved imperfectly for polyphonic music
signals. Interpreted as a computer vision problem in spectrograms,
Convolutional Neural Networks (CNNs) seem to be an ideal fit. On
a dataset of about 100 minutes of music with 26k annotated onsets,
we show that CNNs outperform the previous state-of-the-art while
requiring less manual preprocessing. Investigating their inner workings, we find two key advantages over hand-designed methods: Using separate detectors for percussive and harmonic onsets, and combining results from many minor variations of the same scheme. The
results suggest that even for well-understood signal processing tasks,
machine learning can be superior to knowledge engineering.