  Deep neural networks (DNNs) are successfully applied in a wide variety of
music information retrieval (MIR) tasks but their predictions are usually not
interpretable. We propose audioLIME, a method based on Local Interpretable
Model-agnostic Explanations (LIME) extended by a musical definition of
locality. The perturbations used in LIME are created by switching on/off
components extracted by source separation which makes our explanations
listenable. We validate audioLIME on two different music tagging systems and
show that it produces sensible explanations in situations where a competing
method cannot.
