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.

我们提出了一种新的方法，将 SlotAttention 结构应用于音频领域，实现了无监督的音乐分解。我们介绍了一个基于频谱图的多对象音乐数据集，用于评估西方调性音乐上的对象中心学习。MusicSlots 在无监督的音符发现上表现良好，并在监督音符属性预测任务上优于几种常用对比方法。