Automatic Music Classification and Generation by Emotional and Physiological Responses

ABSTRACT:

The ability to automatically classify music is one of the vital elements of any music information retrieval system. Anyone interested in selling, buying, or researching music could benefit from advances in this area. This work intends to focus on classifying music by the physiological responses it produces in humans. It will employ biofeedback techniques to measure how specific acoustic features affect responses such as breathing, heart rate, and skin temperature. It will also investigate how these physiological responses, as well as a number of other descriptive features, can be used in computerized classification of music by genre and in determining musical similarity. The task of genre classification will be further explored by not only considering large numbers of features, but making refinements to the genre labels themselves using unsupervised learning strategies and dimensionality reduction techniques. This work will also investigate possibilities for computational creativity by using the information obtained through these experiments to generate music tailored to a specific mood.