Elementary sound based audio pattern searching

In the paper, we proposed a novel approach for query-by-example audio patterns searching method inspired by classical phoneme-based word spotting and speech recognition systems, where larger and more complex models are created as a concatenation of pre-trained phoneme sub-models. Unlike most other methods for sound events classification which uses pre-trained sound classes, our system has no default limitations for demanded sound event and any sound example can be added into the search space. Using methods of cluster analysis and Viterbi alignment, we created a database of what we call “elementary sound” models. These models serves as elementary units of sound event and gives our system flexibility and versatility in adding new patterns into query. In the paper, the methodology of elementary sound models database creation and its characteristics are presented. Also process of creating and embedding models of new patterns into the model network is described.