Hybrid Long- and Short-Term Models of Folk Melodies

In this paper, we present the results of a study on dynamic models for predicting sequences of musical pitch in melodies. Such models predict a probability distribution over the possible values of the next pitch in a sequence, which is obtained by combining the prediction of two components (1) a long-term model (LTM) learned offline on a corpus of melodies, as well as (2) a short-term model (STM) which incorporates context-specific information available during prediction. Both the LTM and the STM learn regularities in pitch sequences solely from data. The models are combined in an ensemble, wherein they are weighted by the relative entropies of their respective predictions. Going by previous work that demonstrates the success of Connectionist LTMs, we employ the recently proposed Recurrent Temporal Discriminative Restricted Boltzmann Machine (RTDRBM) as the LTM here. While it is indeed possible for the same model to also serve as an STM, our experiments showed that n-gram models tended to learn faster than the RTDRBM in an online setting and that the hybrid of an RTDRBM LTM and an n-gram STM gives us the best predictive performance yet on a corpus of monophonic chorale and folk melodies.