Programming language design for music

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An Automatic Accompanist Based on Hidden Markov Model

The behavior of a human accompanist is simulated using a hidden Markov model. The model is divided in two levels. The lower level models directly the incoming signal, without requiring analysis techniques that are prone to errors; the higher level models the performance, taking into account all the possible errors made by the musician. Alignment is performed through a decoding technique alternative to classic Viterbi decoding. A novel technique for the training is also proposed. After the performance has been aligned with the score, the information is used to compute local tempo and drive the automatic accomaniment

A connectionist and a traditional AI quantizer, symbolic versus sub-symbolic models of rhythm perception

Contains fulltext : 74722.pdf (publisher's version ) (Open Access)The Symbolic AI paradigm and the Connectionist paradigm have produced some incompatible models of the same domain of cognition. Two such models in the field of rhythm perception, namely the Longuet-Higgins Musical Parser and the Desain & Honing connectionist quantizer, were studied in order to find ways to compare and evaluate them. Different perspectives from which to describe their behavior were developed, providing a conceptual as well as a visual representation of the operation of the models. With these tools it proved possible to discuss their similarities and differences and to narrow the gap between sub-symbolic and symbolic models.Themanr. o.d.t. Music and the Cognitive Science