Towards Musically Meaningful Explanations Using Source Separation

Understand

Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks.

• Such models are usually considered "black boxes", meaning that their predictions are not interpretable.
• Prior work on explainable models in MIR has generally used image processing tools to produce explanations for DNN predictions, but these are not necessarily musically meaningful, or can be listened to (which, arguably, is important in music).
• We propose audioLIME, a method based on Local Interpretable Model-agnostic Explanation (LIME), extended by a musical definition of locality.