Chord Recognition in Symbolic Music: A Segmental CRF Model, Segment-Level Features, and Comparative Evaluations on Classical and Popular Music

Harmonic analysis is an important step towards creating high-level representations of tonal music. High-level structural relationships form an essential component of music analysis, whose aim is to achieve a deep understanding of how music works. At its most basic level, harmonic analysis of music in symbolic form requires the partitioning of a musical input into segments along the time dimension, such that the notes in each segment correspond to a musical chord. This chord recognition task can often be time consuming and cognitively demanding, hence the utility of computer-based implementations. Reflecting historical trends in artificial intelligence, automatic approaches to harmonic analysis have evolved from purely grammar-based and rule-based systems (Wino-grad, 1968; Maxwell, 1992), to systems employing weighted rules and optimization algorithms (T emper-ley and Sleator, 1999; Pardo and Birmingham, 2002; Scholz and Ramalho, 2008; Rocher et al., 2009), to data driven approaches based on supervised machine learning (ML) (Raphael and Stoddard, 2003; Radicioni and Esposito, 2010).