  Current performance evaluation for audio source separation depends on
comparing the processed or separated signals with reference signals. Therefore,
common performance evaluation toolkits are not applicable to real-world
situations where the ground truth audio is unavailable. In this paper, we
propose a performance evaluation technique that does not require reference
signals in order to assess separation quality. The proposed technique uses a
deep neural network (DNN) to map the processed audio into its quality score.
Our experiment results show that the DNN is capable of predicting the
sources-to-artifacts ratio from the blind source separation evaluation toolkit
without the need for reference signals.
