One of the main contributions of this work is the development of a multidisciplinary technique for generating musical pieces with structure and long-term coherence. The MorpheuS system implements a novel mathematical model for calculating tonal tension in music. This model is then used for generating new music with a prespecified, or learned, tension profile. The generated new (affective) music is advances the state-of-the art, as it is optimized to contain repeated patterns and themes of a template piece by using a state-of-the-art pattern detection algorithm. Musical compositions created by MorpheuS have been performed by Prof. Elaine Chew in Stanford, London, Brighton and Cambridge.
Finally, we explored the power of deep learning methods for capturing musical style. These models which, include a semantic vector space model and long-short term models that implements a novel musically inspired image representation, confirm the huge potential of deep learning in this field. The researcher also organized the first International Workshop on Deep Learning and Music and Machine Learning, joint with the International Joint Conference of Neural Networks in Anchorage, US. The presence of 50+ registered participants and speakers from both Google and Pandora, testify to the potential of deep learning techniques for music composition.
In order to facilitate the music research of music analysts and music cognition researchers, and demonstrate the developed tension model, an interactive multimodal website, called IMMA, was developed to visualise both audio and score characteristics in sync with their score and audio performance. We plan to further extend this platform to a widely used music analysis platform and score repository.
