On music genre classification via compressive sampling

Recent work [1] combines low-level acoustic features and random projection (referred to as “compressed sensing” in [1]) to create a music genre classification system showing an accuracy among the highest reported for a benchmark dataset. This not only contradicts previous findings that suggest low-level features are inadequate for addressing high-level musical problems, but also that a random projection of features can improve classification. We reproduce this work and resolve these contradictions.