Comparison of feature selection algorithms for acoustic event detection system

This paper brings the comparison of mutual information based selection algorithms for the acoustic event detection system (EAR TUKE). High dimensional feature vectors were reduced according to the different selection criteria. Proposed features were used to train Hidden Markov Models (HMM), which were evaluated by the Viterbi based decoding algorithm. The comparison of applied selection criteria, their corresponding performances and the identification of convenient features were demonstrated via representative experimental results.