A hierarchical generative model for Generic Audio Document Categorization

In this paper, we call the pattern classification problem that consists in assigning a category label to a long audio signal based on its semantic content as Generic Audio Document Categorization (GADC). A novel generative model is proposed to describe the generic audio document categories and solve the GADC problem. This model is a four-level hierarchical model in which two latent variables "audio topic" and "audio word" are introduced in addition to the two observed variables category and audio feature. We present an iterative learning algorithm including two Expectation-Maximization (EM) cycles to estimate the model parameters and give a discriminative document weighting procedure to make the model more discriminative. Subsequently, the distribution of "audio topic" in the well-trained model is utilized to represent each generic audio document category. This is same with some bag-of-word methods. However, our method is advanced since it does not require quantizing the continuous audio features to a vocabulary of "audio words". Finally, experiment results show the effectiveness of our approach.