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Feedback Module Based Convolution Neural Networks for Sound Event Classification
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DOI：10.1109/ACCESS.2021.3126004.png)
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Abstract
En 中文
Sound event classification is starting to receive a lot of attention over the recent years in the field of audio processing because of open datasets, which are recorded in various conditions, and the introduction of challenges. To use the sound event classification model in the wild, it is needed to be independent of recording conditions. Therefore, a more generalized model, that can be trained and tested in various recording conditions, must be researched. This paper presents a deep neural network with a dual-path frequency residual network and feedback modules for sound event classification. Most deep neural network based approaches for sound event classification use feed-forward models and train with a single classification result. Although these methods are simple to implement and deliver reasonable results, the integration of recurrent inference based methods has shown potential for classification and generalization performance improvements. We propose a weighted recurrent inference based model by employing cascading feedback modules for sound event classification. In our experiments, it is shown that the proposed method outperforms traditional approaches in indoor and outdoor conditions by 1.94% and 3.26%, respectively.
Keywords:
Feature extraction
Task analysis
Hidden Markov models
Neural networks
Convolution
Shape
Residual neural networks
Dual-path residual network
feedback module
recurrent inference
sound event classification
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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