A quantitative comparison of blind C50 estimators

@article{Parada2014AQC,
  title={A quantitative comparison of blind C50 estimators},
  author={Pablo Peso Parada and Dushyant Sharma and Jose Lainez and Daniel Barreda and Patrick A. Naylor and Toon van Waterschoot},
  journal={2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC)},
  year={2014},
  pages={298-302}
}
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… CONTINUE READING

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