Deep Neural Decision Forest for Acoustic Scene Classification

• URL: http://arxiv.org/abs/2203.03436v1
• Date: Mon, 7 Mar 2022 14:39:42 GMT
• Title: Deep Neural Decision Forest for Acoustic Scene Classification
• Authors: Jianyuan Sun, Xubo Liu, Xinhao Mei, Jinzheng Zhao, Mark D. Plumbley, Volkan K{\i}l{\i}\c{c}, Wenwu Wang
• Abstract summary: Acoustic scene classification (ASC) aims to classify an audio clip based on the characteristic of the recording environment. We propose a novel approach for ASC using deep neural decision forest (DNDF)
• Score: 45.886356124352226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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