An Efficient Model for Estimating Subjective Quality of Separated Audio Source Signals

Audio source separation, i.e. the separation of one or more target sources from a given audio signal mixture, has been a vivid and growing research field in recent years. Applications emerge which allow users to manipulate a given music recording to create a personal mix of a music recording or to adapt the audio level of the sports commentator and the atmosphere in sports broadcast to their own preference or hearing abilities. The perceived quality of the produced audio signals is an important key factor to rate these separation systems. In order to optimize them, an efficient, perceptually based measurement scheme to predict the perceived audio quality would be highly beneficial. Existing evaluation models, such as BSSEval or PEASS suffer from poor prediction of perceived quality or excessive computational complexity. In this paper a model for prediction of the perceived audio quality of separated audio source signals is presented, solely based on two timbre features and demanding less computational effort than current perceptual measurement schemes for audio source separation. High correlation of the model output with perceived quality is demonstrated.