MUSIC TAGGING WITH REGULARIZED LOGISTIC REGRESSION
Bo Xie, Wei Bian, Dacheng Tao, Parag Chordia · 2011
In this paper, we present a set of simple and efficient regularized logistic regression algorithms to predict tags of music. We first vector-quantize the delta MFCC features using k-means and construct "bag-of-words" representation for each song. We then learn the parameters of these logistic regression algorithms from the "bag-of- words" vectors and ground truth labels in the training set. At test time, the prediction confidence by the linear classifiers can be used to rank the songs for music annotation and retrieval tasks. Thanks to the convex property of the objective functions, we adopt an efficient and scalable generalized gradient method to learn the parameters, with global optimum guaranteed. And we show that these efficient algorithms achieve stateof- the-art performance in annotation and retrieval tasks evaluated on CAL-500. © 2011 International Society for Music Information Retrieval.