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
Original Titel:
Improved Musical Onset Detection with Convolutional Neural Networks
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 39th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Original Kurzfassung:
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.
Forschungs-