Linear Programming Boosting for Classiﬁcation of Musical Genre
published: Dec. 29, 2007, recorded: December 2007, views: 478
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Classification of musical genre from raw audio files is a fairly well researched area of music research, and as such provides a good starting point for testing a new algorithm. The Music Information Retrieval Evaluation eXchange (MIREX) is a yearly competition in a wide range of machine learning applications in music. MIREX 2005 included a genre classification task, the winner of which  was an application of the multiclass boosting algorithm AdaBoost.MH . It is believed that Linear Programming Boosting (LPBoost) is a more appropriate algorithm for this application due to the higher degree of sparsity in the solutions . The present study aims to improve on the  result by using a similar feature set and the multiclass boosting algorithm LPBoost.MC.
References:  J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and K. Bal´azs. Aggregate features and ADABOOST for music classification. Machine Learning, 65 (2-3):473–484, 2006.  R.E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37:297–336, 1999.  Ayhan Demiriz, Kristin P. Bennett, and John Shawe-Taylor. Linear programming boosting via column generation. Machine Learning, 46(1–3):225–254, 2002.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !