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