Isolating individual instruments in a musical mixture has a myriad of
potential applications, and seems imminently achievable given the levels of
performance reached by recent deep learning methods. While most musical source
separation techniques learn an independent model for each instrument, we
propose using a common embedding space for the time-frequency bins of all
instruments in a mixture inspired by deep clustering and deep attractor
networks. Additionally, an auxiliary network is used to generate parameters of
a Gaussian mixture model (GMM) where the posterior distribution over GMM
components in the embedding space can be used to create a mask that separates
individual sources from a mixture. In addition to outperforming a
mask-inference baseline on the MUSDB-18 dataset, our embedding space is easily
interpretable and can be used for query-based separation.

该论文介绍一种使用深度聚类和深度吸引子网络的方法，将音乐混合中的所有乐器的时频图嵌入到一个共同的嵌入空间中，并使用混合高斯模型生成参数，从而实现对音乐源的分离，同时嵌入空间具有易于解释性。