An N-gram model for unstructured audio signals toward information retrieval

Abstract

An N-gram modeling approach for unstructured audio signals is introduced with applications to audio information retrieval. The proposed N-gram approach aims to capture local dynamic information in acoustic words within the acoustic topic model framework which assumes an audio signal consists of latent acoustic topics and each topic can be interpreted as a distribution over acoustic words. Experimental results on classifying audio clips from BBC Sound Effects Library according to both semantic and onomatopoeic labels indicate that the proposed N-gram approach performs better than using only a bag-of-words approach by providing complementary local dynamic information.