A tandem connectionist model using combination of multi-scale spectro-temporal features for acoustic event detection

Acoustic event detection systems supporting heterogeneous sets of events face the problem of having to characterize them when they have different acoustic properties (transient, stationary, both, etc.), observing this fact even within the acoustic event itself. Moreover, managing large feature vectors with features characterizing different properties of the signal is always difficult. This paper introduces the usage of spectro-temporal fluctuation features in a tandem connectionist approach, modified to generate posterior features separately for each fluctuation scale and then combine the streams to be fed to a classic GMM-HMM model. The experiments explore scale and event wise performance, as well as different stream combination methods, and show that the proposed method outperforms the GMM-HMM baseline as well as recent proposals in the CHIL 2007 evaluation campaign´s related acoustic event detection tasks.