  Current object-centric learning models such as the popular SlotAttention
architecture allow for unsupervised visual scene decomposition. Our novel
MusicSlots method adapts SlotAttention to the audio domain, to achieve
unsupervised music decomposition. Since concepts of opacity and occlusion in
vision have no auditory analogues, the softmax normalization of alpha masks in
the decoders of visual object-centric models is not well-suited for decomposing
audio objects. MusicSlots overcomes this problem. We introduce a
spectrogram-based multi-object music dataset tailored to evaluate
object-centric learning on western tonal music. MusicSlots achieves good
performance on unsupervised note discovery and outperforms several established
baselines on supervised note property prediction tasks.
