# Deep Neural Decision Forest for Acoustic Scene Classification

arXiv: https://arxiv.org/abs/2203.03436 · HTML: https://mlsanity.com/paper/2203.03436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning](https://mlsanity.com/paper/2107.09998/) (2021)
- [Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes](https://mlsanity.com/paper/1711.01369/) (2017)
- [An Ensemble of Convolutional Neural Networks for Audio Classification](https://mlsanity.com/paper/2007.07966/) (2020)
- [AclNet: efficient end-to-end audio classification CNN](https://mlsanity.com/paper/1811.06669/) (2018)
- [Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices](https://mlsanity.com/paper/2103.03483/) (2021)
- [Label Tree Embeddings for Acoustic Scene Classification](https://mlsanity.com/paper/1606.07908/) (2016)

## Built on by (most cited first, 3 of 3)

- [Simple Pooling Front-ends For Efficient Audio Classification](https://mlsanity.com/paper/2210.00943/) (2022)
- [Continual Learning For On-Device Environmental Sound Classification](https://mlsanity.com/paper/2207.07429/) (2022)
- [Automated Audio Captioning via Fusion of Low- and High- Dimensional Features](https://mlsanity.com/paper/2210.05037/) (2022)

## Abstract (arXiv)

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.