Sound monitoring based on the generalized probabilistic descent method

We propose a method for sound monitoring, which enables one to selectively detect unexpected irregular sounds and ignore the other sounds, i.e., regular sounds. The proposed method is based on the generalized probabilistic descent (GPD) method, which was originally developed as a general concept for the discriminative design of pattern recognizers, and is referred to as minimum detection error (MDE) training. The formulation and implementation of MDE training are described in detail, and its utility is demonstrated in a task of detecting irregular events; more specifically, sounds due to the mis-operation of a tool in a noisy environment