A hybrid graphical model for rhythmic parsing
Artificial Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Training conditional random fields via gradient tree boosting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Algorithms for Chordal Analysis
Computer Music Journal
Computer Music Journal
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A case based approach to expressivity-aware tempo transformation
Machine Learning
The Cognition of Basic Musical Structures
The Cognition of Basic Musical Structures
CarpeDiem: an algorithm for the fast evaluation of SSL classifiers
Proceedings of the 24th international conference on Machine learning
Trip Around the HMPerceptron Algorithm: Empirical Findings and Theoretical Tenets
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
A conditional model for tonal analysis
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Trip Around the HMPerceptron Algorithm: Empirical Findings and Theoretical Tenets
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
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We have recently presented CarpeDiem, an algorithm that can be used for speeding up the evaluation of Supervised Sequential Learning (SSL) classifiers. CarpeDiemprovides 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.