A quantitative comparison of blind C50 estimators

The problem of blind estimation of the room acoustic clarity index C₅₀ from single-channel reverberant speech signals is presented in this paper. We analyze the performance of several machine learning methods for a regression task using 309 features derived from the speech signal and modeled with a Deep Belief Network (DBN), Classification And Regression Tree (CART) and Linear Regression (LR). These techniques are evaluated on a large test database (86 hours) that includes babble noise and reverberation using both artificial and real room impulses responses (RIRs). All methods are trained on a database which contains noise, speech and simulated RIRs different from the test set. The performance results show that the DBN model gives the lowest error for the simulated RIRs whereas the LR model gives the best generalization performance with the highest accuracy for real RIRs.