The group’s research is broadly divided into two areas: Music Information Retrieval (MIR), which is primarily concerned with the automatic extraction and modeling of musical information from the audio signal, and Music Cognition, which seeks to understand the psychological experience of music.

Our research in MIR is driven by two guiding principles:

1. The next generation of music technologies will be based on machine understanding of music, helping us to find music we like from a sea of content and create music with more intuitive and powerful tools. More people will be able to engage with and create music, experiencing and sharing its emotional benefits, and there will be new modes of expression for performers and composers.

2. Computational modeling of music will help us to understand human creativity and lead to new creative partnerships between musicians as well as between musicians and computers. I am particularly interested in musical improvisation.

Our music cognition is driven by the beliefs that:

1. By understanding our complex multi-layered response to music, we will begin to see the connection between abstract sound patterns and the profoundly moving experiences that music can engender.

2. Because music involves so many aspects of cognition, from complex auditory pattern recognition, to attention, memory and emotion, its understanding will provide a window into the mind.

MIR and music cognition are complementary perspectives in researching the fundamental mystery of music. By combining a close analysis of musical sound and structure with the knowledge of how the brain processes music, we will begin to understand how our experience of music is created. This will enable more musically intelligent systems to be built.