Minimum detection error training for acoustic signal monitoring

In this paper we propose a novel approach to the detection of acoustic irregular signals using minimum detection error (MDE) training. The MDE training is based on the generalized probabilistic descent method, which was originally developed as a general concept for a discriminative pattern recognizer design. We demonstrate its fundamental utility by experiments in which several acoustic events are detected in a noisy environment