Mid-level Chord Transition Features for Musical Style Analysis
Abstract: Chords and their progressions are an important aspect of Western tonal music. Specifically, transitions between subsequent chords within a piece carry style-relevant information. To extract such information from audio recordings, a naive approach first peforms automatic chord estimation for computing chord labels explicitly and then derives transition statistics. Often, this is done with Hidden Markov Models involving the Viterbi decoding algorithm. However, since chords are often ambiguous, deciding on one “optimal” chord sequence can be problematic, which heavily affects the subsequent derivation of transition features. In this paper, we propose novel mid-level features that capture chord transitions in a “soft” way. Our method exploits the Baum-Welch algorithm, which does not involve hard decisions on chord labels. Instead, we obtain probabilistic features that account for ambiguities among chords and chord transitions. In several experiments, we evaluate these features within a style classification scenario discriminating four historical periods of Western classical music. Our soft transition features consistently achieve higher accuracies than comparable hard-decision features, thus demonstrating the descriptive power of the novel features.
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