Style-Independent Computer-Assisted Exploratory Analysis of Large Music Collections Büyük Müzik Koleksiyonlarinin Biçemden Baimsiz Bilgisayar Destekli Keif Niteliinde Çözümlenmesi

The first goal of this paper is to introduce musicologists and music theorists to the benefits offered by state-of-the-art pattern recognition techniques. The second goal is to provide them with a computer-based frame- work that can be used to study large and di- verse collections of music for the purposes of empirically developing, exploring and vali- dating theoretical models. The software pre- sented in this paper implements techniques from the fields of machine learning, pattern recognition and data mining applied to and considered from the perspectives of music theory and musicology. An important priority underpinning the software presented here is the ability to apply it to a much wider range of art, folk and popular musics of the world than is possible using the types of computer-based approaches traditionally used in music re- search. The tools and techniques presented here will thus enable exploratory research that can aid in the formation and validation of theoretical models for types of music for which such models have been elusive to date. These tools will also allow research on form- ing theoretical links spanning types of music that have traditionally been studied as distinct groups. A particular emphasis is placed on the importance of performing studies involving many pieces of music, rather than just a few compositions that may not in fact be truly representative of the overall corpus under consideration.