Oracle estimators for the benchmarking of source separation algorithms

Source separation is a difficult problem for which many algorithms have been proposed. In this article, we define oracle estimators which compute the best performance achievable by different classes of algorithms on a given mixture, in a theoretical evaluation framework where the reference sources are available. We describe explicit oracle estimators for four particular classes of algorithms: beamforming, single-channel time-frequency masking, multichannel time-frequency masking and best basis masking. We evaluate their performance on various audio mixtures and study their robustness. We draw several conclusions regarding the performance bounds of blind algorithms, the choice of the best class of algorithms and the assessment of the separation difficulty. In particular, we show that it is worth developing blind time-frequency masking algorithms relaxing the common assumption of a single active source per time-frequency point.