Tonal Harmony Analysis: A Supervised Sequential Learning Approach

We have recently presented CarpeDiem, an algorithm that can be used for speeding up the evaluation of Supervised Sequential Learning (SSL) classifiers. CarpeDiem provides impressive time performance gain over the state-of-art Viterbi algorithm when applied to the tonal harmony analysis task. Along with interesting computational features, the algorithm reveals some properties that are of some interest to Cognitive Science and Computer Music. To explore the question whether and to what extent the implemented system is suitable for cognitive modeling, we first elaborate about its design principles, and then assess the quality of the analyses produced. A threefold experimentation reviews the learned weights, the classification errors, and the search space in comparison to the actual problem space; data about these points are reported and discussed. AI in Art and Music; Cognitive Modeling; Machine learning; Music Analysis.