Local Interpretable Model Agnostic Explanations for Music Content Analysis
The interpretability of a machine learning model is essential for gaining insight into model behaviour. While some machine learning models (e.g. decision trees) are transparent, the majority of models used today are still black boxes. Recent work in machine learning aims to analyse these models by explaining the basis of their decisions. In this work, we extend one such technique, called local interpretable model agnostic explanations, to music content analysis. We propose three versions of explanations: one version is based on temporal segmentation, and the other two are based on frequency and time frequency segmentation. These explanations provide meaningful ways to understand the factors that influence the classification of specific input data. We apply our proposed methods to three singing voice detection systems: the first two are designed using decision tree and random forest classifiers, respectively; the third system is based on convolutional neural network. The explanations we generate provide insights into the model behaviour. We use these insights to demonstrate that despite achieving 71.4% classification accuracy, the decision tree model fails to generalise. We also demonstrate that the model agnostic explanations for the neural network model agree in many cases with the model dependent saliency maps. The experimental code and results are available online. 1
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