Hierarchical modeling using automated sub-clustering for sound event recognition

The automatic recognition of sound events allows for novel applications in areas such as security, mobile and multimedia. In this work we present a hierarchical hidden Markov model for sound event detection that automatically clusters the inherent structure of the events into sub-events. We evaluate our approach on an IEEE audio challenge dataset consisting of office sound events and provide a systematic comparison of the various building blocks of our approach to demonstrate the effectiveness of incorporating certain dependencies in the model. The hierarchical hidden Markov model achieves an average frame-based F-measure recognition performance of 45.5% on a test dataset that was used to evaluate challenge submissions. We also show how the hierarchical model can be used as a meta-classifier, although in the particular application this did not lead to an increase in performance on the test dataset.