This paper proposes a universal sound separation (USS) method capable of
handling untrained sampling frequencies (SFs). The USS aims at separating
arbitrary sources of different types and can be the key technique to realize a
source separator that can be universally used as a preprocessor for any
downstream tasks. To realize a universal source separator, there are two
essential properties: universalities with respect to source types and recording
conditions. The former property has been studied in the USS literature, which
has greatly increased the number of source types that can be handled by a
single neural network. However, the latter property (e.g., SF) has received
less attention despite its necessity. Since the SF varies widely depending on
the downstream tasks, the universal source separator must handle a wide variety
of SFs. In this paper, to encompass the two properties, we propose an
SF-independent (SFI) extension of a computationally efficient USS network,
SuDoRM-RF. The proposed network uses our previously proposed SFI convolutional
layers, which can handle various SFs by generating convolutional kernels in
accordance with an input SF. Experiments show that signal resampling can
degrade the USS performance and the proposed method works more consistently
than signal-resampling-based methods for various SFs.

该论文提出了一种能够处理未经训练的采样频率的通用音频分离（USS）方法，旨在分离任意类型的来源，可作为任何后续任务的预处理器的关键技术，并通过实验验证了该方法相对于基于信号重采样的方法在各种采样频率下的性能优越性。