Random forest regression based acoustic event detection with bottleneck features

Abstract

This paper deals with random forest regression based acoustic event detection (AED) by combining acoustic features with bottleneck features (BN). The bottleneck features have a good reputation of being inherently discriminative in acoustic signal processing. To deal with the unstructured and complex real-world acoustic events, an acoustic event detection system is constructed using bottleneck features combined with acoustic features. Evaluations were carried out on the UPC-TALP and ITC-Irst databases which consist of highly variable acoustic events. Experimental results demonstrate the usefulness of the low-dimensional and discriminative bottleneck features with relative 5.33% and 5.51% decreases in error rates respectively.