Linear Programming Boosting for Classification of Musical Genre

author: Tom Diethe, Department of Computer Science, University College London
published: Dec. 29, 2007,   recorded: December 2007,   views: 4344


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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 [1] was an application of the multiclass boosting algorithm AdaBoost.MH [2]. 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 [3]. The present study aims to improve on the [1] result by using a similar feature set and the multiclass boosting algorithm LPBoost.MC.
References: [1] 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. [2] R.E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37:297–336, 1999. [3] Ayhan Demiriz, Kristin P. Bennett, and John Shawe-Taylor. Linear programming boosting via column generation. Machine Learning, 46(1–3):225–254, 2002.

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