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Deep learning has been applied to diverse audio semantics tasks, enabling theconstruction of models that learn hierarchical levels of features fromhigh-dimensional raw data, delivering state-of-the-art performance. But dothese algorithms perform similarly in real-world conditions, or just at thebenchmark, where their high learning capability assures the completememorization of the employed datasets? This work presents a deep learningfront-end, aiming at discarding detrimental information before entering themodeling stage, bringing the learning process closer to the point, anticipatingthe development of robust and context-agnostic classification algorithms.

PERSA+: A Deep Learning Front-End for Context-Agnostic Audio Classification