The task of cover song identification is a relevant one in the field of Music Informatics Research. We have been working on methods to identify covers in large datasets, more specifically on the Million Song Dataset. Our approach, that makes use of various machine learning algorithms (sparse coding, linear discriminative analysis), outperforms previous results for this same task.
This project was supported by the “Caja Madrid” Foundation and the NSF.
Eric J Humphrey, Oriol Nieto, Juan P Bello
Data Driven and Discriminative Projections for Large-scale Cover Song Identification
Proc. of the 14th International Society for Music Information Retrieval Conference Curitiba, Brazil,2013