  Acoustic scene classification (ASC) aims to classify an audio clip based on
the characteristic of the recording environment. In this regard, deep learning
based approaches have emerged as a useful tool for ASC problems. Conventional
approaches to improving the classification accuracy include integrating
auxiliary methods such as attention mechanism, pre-trained models and ensemble
multiple sub-networks. However, due to the complexity of audio clips captured
from different environments, it is difficult to distinguish their categories
without using any auxiliary methods for existing deep learning models using
only a single classifier. In this paper, we propose a novel approach for ASC
using deep neural decision forest (DNDF). DNDF combines a fixed number of
convolutional layers and a decision forest as the final classifier. The
decision forest consists of a fixed number of decision tree classifiers, which
have been shown to offer better classification performance than a single
classifier in some datasets. In particular, the decision forest differs
substantially from traditional random forests as it is stochastic,
differentiable, and capable of using the back-propagation to update and learn
feature representations in neural network. Experimental results on the
DCASE2019 and ESC-50 datasets demonstrate that our proposed DNDF method
improves the ASC performance in terms of classification accuracy and shows
competitive performance as compared with state-of-the-art baselines.
