Unsupervised learning of low-level audio features for music similarity estimation
Christian Osendorfer, Jan Schlüter, Jürgen Schmidhuber, Patrick van der Smagt · elib (German Aerospace Center) · 2011
While there is an enormous amount of music data available, the field of music analysis almost exclusively uses manually designed features.In this work we learn features from music data in a completely unsupervised way and evaluate them on a musical genre classification task.We achieve results very close to state-of-the-art performance which relies on highly hand-tuned feature extractors.