Inspired by representations used in music cognition studies and computational musicology, we propose three simple and interpretable descriptors for use in mid-to high-level computational analysis of musical audio and applications in content-based retrieval. We also argue that the task of scalable cover song retrieval is very suitable for the development of descriptors that effectively capture musical structures at the song level. The performance of the proposed descriptions in a cover song problem is presented. We further demonstrate that, due to the musically-informed nature of the descriptors, an independently established model of stability and variation in covers songs can be integrated to improve performance.