Room volume classification from room impulse response using statistical pattern recognition and feature selection

Classification of the room volume from the room impulse response (RIR) can be useful in acoustic scene analysis applications, using RIR that is provided directly, or estimated from audio recordings. Current methods for estimating the room volume from the RIR require the source-to-receiver distance, and may be sensitive to differences in absorption. A room volume classification method is presented that does not require the source-to-receiver distance, and which is potentially robust to differences in absorption. Room volume features are defined that are related to the room volume and may be extracted from the RIR. Gaussian mixture models are trained to model room volume classes. Room volume is classified according to a maximum likelihood criterion that is normalized with a background model. Feature selection is performed with different classification error criteria. Both simulated and measured RIRs were examined, achieving an equal error rate of 0.1% and 19.1%, respectively.