Improved Audio Scene Classification Based on Label Label-Tree Tree Embeddings and Convolutional Neural Networks
Abstract: In this paper, we present an efficient approach for audio scene classification. We aim at learning representations for scene examples by exploring the structure of their class labels. A category taxonomy is automatically learned by collectively optimizing a tree-structured structured clustering of the given labels into multiple metaclasses. A scene recording is then transformed into a label label-tree tree embedding image. Elements of the image represent the likelihoods that the scene instance belongs to the metaclasses. We inve investigate stigate classification with label-tree label embedding features learned from different low low-level level features as well as their fusion. We show that the combination of multiple features is essential to obtain good performance. While averaging label label-tree embedding images ges over time yields good performance, we argue that average pooling possesses an intrinsic shortcoming. We alternatively propose an improved classification scheme to bypass this limitation. We aim at automatically learning common templates that are useful for the classification task from these images using simple but tailored convolutional neural networks. The trained networks are then employed as a feature extractor that matches the learned templates across a label-tree label embedding image and produce the max maximum imum matching scores as features for classification. Since audio scenes exhibit rich content, template learning and matching on low-level level features would be inefficient. With label label-tree tree embedding