My abstract was selected at the British Computer Society’s 5th Doctoral Consortium and I made a presentation, titled “A Neural Probabilistic Model for Music Prediction” on May 16, 2013. The abstract for the talk is the following:
“Neural Networks and Markov models have received long-standing attention in music prediction. The latter, while being successful at modelling the joint probability of short musical sequences, suffer from problems pertaining to the curse of dimensionality and zero-occurrence when the sequences become longer. We present a new model for music prediction based on the Restricted Boltzmann Machine (RBM) and evaluate it on sequences of musical pitch in a corpus of monophonic MIDI melodies. The results show that this model is able to make use of information present in longer sequences more effectively than recently evaluated Markov models, outperforming them on the said corpus while also scaling gracefully in the required number of free parameters. While initial results have been encouraging, there is also scope for considering more powerful models that build upon this basic architecture. Some questions that hope to be addressed in the future are whether such models can provide an insight into the development of musical taste, make predictions about more complex musical structures that involve polyphony and variations in rhythm, be of use in music education and compositional assistance, and aid in Music Information Retrieval tasks such as music transcription and classification.”
I have also attached a PDF copy of the presentation (all made in Beamer/LaTeX) below.