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arXiv · 1811.00454

Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks

Emad M. Grais·Hagen Wierstorf·Dominic Ward·Russell Mason·Mark D. Plumbley
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Abstract

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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BibTeXRIS

Emad M. Grais, Hagen Wierstorf, Dominic Ward, Russell Mason, Mark D. Plumbley. 2018-11-01. Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks. https://arxiv.org/abs/1811.00454

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