An Interpretable Deep Learning Model for Automatic Sound Classification

@article{Zinemanas2021AnID,
  title={An Interpretable Deep Learning Model for Automatic Sound Classification},
  author={Pablo Zinemanas and Mart{\'i}n Rocamora and Marius Miron and Frederic Font and Xavier Serra},
  journal={Electronics},
  year={2021},
  volume={10},
  pages={850}
}
Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. There is still a lack of research in the audio domain, despite the increasing interest in developing deep learning models that provide explanations of their decisions… 

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