Tensor semantic model for an audio classification system

Abstract

Audio resources are a very important part of multimedia information. The classification effect of audio is directly related to the service mode of personal resource management systems. At present, vector features have been widely used in audio classification systems. However, some semantic correlations among different audio information can not be completely expressed by simple vector representation. Tensors are multidimensional matrices, and their mathematical expansion and application can express multi-semantic information. The tensor uniform content locator (TUCL) is proposed as a means of expressing the semantic information of audio, and a three-order tensor semantic space is constructed according to the semantic tensor. Tensor semantic dispersion (TSD) can aggregate some audio resources with the same semantics and, at the same time, its automatic classification can be accomplished by calculating the TSD. In order to effectively utilize TSD classification information, a radial basis function tensor neural network (RBFTNN) is constructed and used to train an intelligent learning model. Experimental results show that the tensor model can significantly improve the classification precision under multi-semantic classification requests within an information resource management system.