Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks

1 Nov 2018  ·  Emad M. Grais, Hagen Wierstorf, Dominic Ward, Russell Mason, Mark D. Plumbley ·

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.

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