Mid-level Chord Transition Features for Musical Style Analysis
Chords and their progressions are an important tonal music. Specifically, transitions between within a piece carry style-relevant information. formation from audio recordings, a naive approa tomatic chord estimation for computing chord la then derives transition statistics. Often, this is Markov Models involving the Viterbi decoding al since chords are often ambiguous, deciding on o sequence can be problematic, which heavily aff derivation of transition features. In this paper, mid-level features that capture chord transitions i method exploits the Baum-Welch algorithm, whi hard decisions on chord labels. Instead, we obtai tures that account for ambiguities among chord tions. In several experiments, we evaluate thes style classification scenario discriminating four h Western classical music. Our soft transition fe achieve higher accuracies than comparable hard thus demonstrating the descriptive power of the n.