Digital Music Lab – Analysing Big Music Data is an AHRC project funded under the Big Data call of the Digital Transformations in the Arts and Humanities Theme. Our goal is to develop research methods and software infrastructure for exploring and analysing large-scale music collections, and to provide researchers and users with datasets and computational tools to analyse music audio, scores and metadata.
Current progress on the DML project will be presented at the ‘Statistical Musicology’ session of the European Conference on Data Analysis (ECDA 2014). ECDA will take place on 2-4 July in Bremen, Germany. Project-related talks are listed below:
- Dan Tidhar, Srikanth Cherla, Daniel Wolff, and Tillman Weyde, “An iterative learning approach to dataset demarcation in music analysis”
- Tillman Weyde, Stephen Cottrell, Emmanouil Benetos, Daniel Wolff, Dan Tidhar, Jason Dykes, Mark Plumbley, Simon Dixon, Mathieu Barthet, Nicolas Gold, Samer Abdallah, and Mahendra Mahey, “Digital Music Lab – A Framework for Analysing Big Music Data”
- Srikanth Cherla, Dan Tidhar, Artur d’Avlia Garcez, and Tillman Weyde, “Machine Learning for the Analysis of a Large Collection of Musical Scales”