Audio-Based Cover Song Retrieval Using Approximate Chord Sequences: Testing Shifts, Gaps, Swaps and Beats

This paper presents a variation on the theme of using string alignment for MIR in the context of cover song identification in audio collections. Here, the strings are derived from audio by means of HMM-based chord estimation. The characteristics of the cover-song ID problem and the nature of common chord estimation errors are carefully considered. As a result strategies are proposed and systematically evaluated for key shifting, the cost of gap insertions and character swaps in string alignment, and the use of a beat-synchronous feature set. Results support the view that string alignment, as a mechanism for audiobased retrieval, cannot be oblivious to the problems of robustly estimating musically-meaningful data from audio.