My paper was accepted at the 6th International Workshop on Machine Learning and Music (MML 2013) held in conjunction with the European Conference on Machine Learning (ECML 2013) this year. The paper is titled, “A Neural Probabilistic Model for Predicting Melodic Sequences”. Its abstract is the following:
“We present an approach for modelling melodic sequences using Restricted Boltzmann Machines, with an application to folk melody classification. Results show that this model’s predictive performance is slightly better in our experiment than that of previously evaluated n-gram models. The model has a simple structure and in our evaluation it scaled linearly in the number of free parameters with length of the modelled context. A set of these models is used to classify 7 different styles of folk melodies with an accuracy of 61.74%.”
Unfortunately, I was unable to go to the conference myself so my colleague Emmanouil presented the paper on my behalf. In addition to this, another paper I co-authored with Emmanouil, titled “An Efficient Shift-Invariant Model for Polyphonic Music Transcription” was also accepted into the same workshop which he presented as well.
I’m including copies of the paper and the presentation below.