Linear Programming Boosting for Classification of Musical Genre
Tom Diethe, John S. Shawe-Taylor · 2007
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. 1