Feature selection for room volume identification from room impulse response

The room impulse response (RIR) can be used to calculate many room acoustical parameters, such as the reverberation time (RT). However, estimating the room volume, another important room parameter, from the RIR is typically a more difficult task requiring extraction of other features from the RIR. Most of the existing fully-blind methods for estimating the room volume from the RIR do not combine features from different feature sets. This can be one reason to the fact that these methods are sensitive to differences in source-to-receiver distance and wall reflection coefficients. We propose a new approach in which hypothetical-volume room models are trained with room volume features from different feature sets. Estimation is performed by identifying the hypothesis with maximum-likelihood (ML) using background model normalization. The different feature sets are compared using equal error rate (EER) of hypothesis verification. A combination of features from the different feature sets is selected so that minimum EER is achieved. Using the selected features, we achieve average detection rate of 98.8% with a standard deviation (STD) of 1.5% for eight rooms with different volumes, source-to-receiver distances, and wall reflection coefficients.