Audio feature engineering for automatic music genre classification
Paolo Annesi, Roberto Basili, Raffaele Gitto, Alessandro Moschitti, Riccardo Petitti · 2007
The scenarios opened by the increasing availability, sharing and dissemination of music across the Web is pushing for fast, effective and abstract ways of organizing and retrieving music material. Automatic classification is a central activity to model most of these processes, thus its design plays a relevant role in advanced Music Information Retrieval. In this paper, we adopted a state-of-the-art machine learning algorithm, i.e. Support Vector Machines, to design an automatic classifier of music genres. In order to optimize classification accuracy, we implemented some already proposed features and engineered new ones to capture aspects of songs that have been neglected in previous studies. The classification results on two datasets suggest that our model based on very simple features reaches the state-of-art accuracy (on the ISMIR dataset) and very high performance on a music corpus collected locally. 1