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A novel acoustic scene classification model using the late fusion of convolutional neural networks and different ensemble classifiers
Applied Acoustics ( IF 3.4 ) Pub Date : 2021-04-01 , DOI: 10.1016/j.apacoust.2020.107829
Mahmoud A. Alamir

Abstract Recent evidence suggests that convolutional neural networks (CNNs) can model acoustic scene classification (ASC) with high accuracy. Ensemble classifiers have also shown high accuracy in different machine learning areas. However, little is known about fusion models between CNNs and different ensemble classifiers for ASC. This study presents an enhanced CNN classification model using the late fusion between CNNs and ensemble classifiers to predict different classes of acoustic scenes. A CNN model was first built to classify fifteen acoustic scene environments. Different ensemble classifier models were then used for this classification problem. Late fusion of CNN and ensemble classifier models was then applied. The results showed that late fusion models have higher classification accuracy, as compared to individual CNN or ensemble classifier models. The best model was obtained by fusion of the CNN and discriminant random subspace classifier with an increase in the average accuracy of 10% as compared to the average accuracy of the CNN model. When compared with previous research on ASC, the late fusion model between CNN and ensemble classifiers showed higher accuracy. Therefore, this method has robust applicability for future ASC problems.

中文翻译:

使用卷积神经网络和不同集成分类器的后期融合的新型声学场景分类模型

摘要 最近的证据表明,卷积神经网络 (CNN) 可以高精度地模拟声场景分类 (ASC)。集成分类器在不同的机器学习领域也表现出很高的准确性。然而,我们对 CNN 和不同的 ASC 集成分类器之间的融合模型知之甚少。本研究提出了一种增强的 CNN 分类模型,使用 CNN 和集成分类器之间的后期融合来预测不同类别的声学场景。首先构建了一个 CNN 模型来对 15 个声学场景环境进行分类。然后将不同的集成分类器模型用于该分类问题。然后应用了 CNN 和集成分类器模型的后期融合。结果表明,后期融合模型具有更高的分类准确率,与单个 CNN 或集成分类器模型相比。最好的模型是通过 CNN 和判别式随机子空间分类器的融合获得的,与 CNN 模型的平均准确度相比,平均准确度提高了 10%。与之前对 ASC 的研究相比,CNN 和集成分类器之间的后期融合模型显示出更高的准确性。因此,该方法对于未来的 ASC 问题具有强大的适用性。
更新日期:2021-04-01
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