  Audio source separation is often used as preprocessing of various
applications, and one of its ultimate goals is to construct a single versatile
model capable of dealing with the varieties of audio signals. Since sampling
frequency, one of the audio signal varieties, is usually application specific,
the preceding audio source separation model should be able to deal with audio
signals of all sampling frequencies specified in the target applications.
However, conventional models based on deep neural networks (DNNs) are trained
only at the sampling frequency specified by the training data, and there are no
guarantees that they work with unseen sampling frequencies. In this paper, we
propose a convolution layer capable of handling arbitrary sampling frequencies
by a single DNN. Through music source separation experiments, we show that the
introduction of the proposed layer enables a conventional audio source
separation model to consistently work with even unseen sampling frequencies.
