Over the last years, automatic music classification has become a standard benchmark problem in the machine learning community. This is partly due to its inherent difficulty, and also to the impact that a fully automated classification system can have in a commercial application. In this paper we test the efficiency of a relatively new learning tool, Extreme Learning Machines (ELM), for several classification tasks on publicly available song datasets. ELM is gaining increasing attention, due to its versatility and speed in adapting its internal parameters. Since both of these attributes are fundamental in music classification, ELM provides a good alternative to standard learning models. Our results support this claim, showing a sustained gain of ELM over a feedforward neural network architecture. In particular, ELM provides a great decrease in computational training time, and has always higher or comparable results in terms of efficiency. © 2013 University of Trieste and University of Zagreb.
Music classification using extreme learning machines / Scardapane, Simone; Comminiello, Danilo; Scarpiniti, Michele; Uncini, Aurelio. - In: ISPA. - ISSN 1845-5921. - (2013), pp. 377-381. (Intervento presentato al convegno 8th International Symposium on Image and Signal Processing and Analysis, ISPA 2013 tenutosi a Trieste; Italy nel 4 September 2013 through 6 September 2013) [10.1109/ispa.2013.6703770].
Music classification using extreme learning machines
SCARDAPANE, SIMONE;COMMINIELLO, DANILO;SCARPINITI, MICHELE;UNCINI, Aurelio
2013
Abstract
Over the last years, automatic music classification has become a standard benchmark problem in the machine learning community. This is partly due to its inherent difficulty, and also to the impact that a fully automated classification system can have in a commercial application. In this paper we test the efficiency of a relatively new learning tool, Extreme Learning Machines (ELM), for several classification tasks on publicly available song datasets. ELM is gaining increasing attention, due to its versatility and speed in adapting its internal parameters. Since both of these attributes are fundamental in music classification, ELM provides a good alternative to standard learning models. Our results support this claim, showing a sustained gain of ELM over a feedforward neural network architecture. In particular, ELM provides a great decrease in computational training time, and has always higher or comparable results in terms of efficiency. © 2013 University of Trieste and University of Zagreb.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.