  Current ML models for music emotion recognition, while generally working
quite well, do not give meaningful or intuitive explanations for their
predictions. In this work, we propose a 2-step procedure to arrive at
spectrogram-level explanations that connect certain aspects of the audio to
interpretable mid-level perceptual features, and these to the actual emotion
prediction. That makes it possible to focus on specific musical reasons for a
prediction (in terms of perceptual features), and to trace these back to
patterns in the audio that can be interpreted visually and acoustically.
