Despite significant recent progress across multiple subtasks of audio source separation, few music source separation systems support separation beyond the four-stem vocals, drums, bass, and other (VDBO) setup. Of the very few current systems that support source separation beyond this setup, most continue to rely on an inflexible decoder setup that can only support a fixed pre-defined set of stems. Increasing stem support in these inflexible systems correspondingly requires increasing computational complexity, rendering extensions of these systems computationally infeasible for long-tail instruments. In this work, we propose Banquet, a system that allows source separation of multiple stems using just one decoder. A bandsplit source separation model is extended to work in a query-based setup in tandem with a music instrument recognition PaSST model. On the MoisesDB dataset, Banquet, at only 24.9 M trainable parameters, approached the performance level of the significantly more complex 6-stem Hybrid Transformer Demucs on VDBO stems and outperformed it on guitar and piano. The query-based setup allows for the separation of narrow instrument classes such as clean acoustic guitars, and can be successfully applied to the extraction of less common stems such as reeds and organs. Implementation is available at https://github.com/kwatcharasupat/query-bandit.

提出了使用一个解码器进行多个音频源分离的系统Banquet，通过在与音乐乐器识别模型PaSST配合的询问式设置中扩展了波段分离源分离模型。使用MoisesDB数据集，Banquet在仅仅24.9百万可训练参数的情况下，接近了复杂得多的6个音频源Hybrid Transformer Demucs系统在VDBO音频源上的性能，并在吉他和钢琴上表现出色。询问式设置允许分离狭窄的乐器类别，如清晰的原声吉他，并可成功应用于提取较少常见的音频源，如簧片和风琴。实现可在以上URL链接处获取。

面向音乐源分离的无关音频轨道限定的单解码器系统