Audio Scene Understanding using Topic Models
Samuel Kim, Shiva Sundaram, Panayiotis Georgiou, Shrikanth Shri Narayanan · Neural Information Processing Systems · 2009
This paper introduces a method to apply the topic models in an audio scene understanding framework. Assuming that an audio signal consists of latent topics that generate acoustic words describing an audio scene, we propose to use a vector quantization method to build an acoustic word dictionary. The classification experiments with semantic labels yield promising results of using the topic models, compared to the conventional GMM-based approach, in audio scene understanding tasks.