A detection of danger sounds based on variable-state hidden Markov models

Abstract

To detect hazardous situations with danger sounds, the acoustic surveillance system is an ideal candidate. The conventional systems recognize environmental sounds with hidden Markov model (HMM) in order to detect danger sounds. It is however difficult to accurately recognize the environmental sounds, because the optimum HMM parameters for environmental sounds have not been identified. It is important factor for accurately recognizing them to ideally determine the number of states, one of the HMM parameter. On the other hand, environmental sounds which include danger sounds have a wider characteristic as the structure, the complexity, the length and etc. The variable states should be therefore an optimum HMM structure to detect the danger sounds. We thus propose the danger sound detection based on variable-state HMMs corresponding to a number of inflection points with the delta power of environmental sounds. We first investigate the recognition performance of environmental sounds including danger sounds with various states of HMM. We then investigate the relationship between the recognition performance and a number of inflection points with the delta power of various environmental sounds. As a result of evaluation experiments, we designed an optimum variable-state HMM for environmental sounds and confirmed the effectiveness of the proposed method.