Hierarchical learning for DNN-based acoustic scene classification

Understand

In this paper, we present a deep neural network (DNN)-based acoustic scene classification framework.

• Two hierarchical learning methods are proposed to improve the DNN baseline performance by incorporating the hierarchical taxonomy information of environmental sounds.
• Firstly, the parameters of the DNN are initialized by the proposed hierarchical pre-training.
• Multi-level objective function is then adopted to add more constraint on the cross-entropy based loss function.