Deep learning has been applied to diverse audio semantics tasks, enabling the construction of models that learn hierarchical levels of features from high-dimensional raw data, delivering state-of-the-art performance. But do these algorithms perform similarly in real-world conditions, or just at the benchmark, where their high learning capability assures the complete memorization of the employed datasets? This work presents a deep learning front-end, aiming at discarding detrimental information before entering the modeling stage, bringing the learning process closer to the point, anticipating the development of robust and context-agnostic classification algorithms.

中文翻译：

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PERSA+：用于上下文无关音频分类的深度学习前端

深度学习已应用于各种音频语义任务，能够构建从高维原始数据中学习特征层次级别的模型，从而提供最先进的性能。但是，这些算法在现实世界中的表现是否相似，或者只是在基准测试中，它们的高学习能力确保了所用数据集的完全记忆？这项工作提出了一个深度学习前端，旨在在进入建模阶段之前丢弃有害信息，使学习过程更接近点，预测稳健和上下文无关的分类算法的发展。