In this paper, we describe a system for transcribing polyphonic drum sequences from an acoustic signal to a symbolic representation. Low-level signal analysis is done with an acoustic model consisting of a Gaussian mixture model and a support vector machine. For higher-level modeling, periodic N-grams are proposed to construct a "language model" for music, based on the repetitive nature of musical structure. Also, a technique for estimating relatively long N-grams is introduced. The performance of N-grams in the transcription was evaluated using a database of realistic drum sequences from different genres and yielded a performance increase of 7.6 % compared to a the use of only prior (unigram) probabilities with the acoustic model.

In this paper, a cost-sensitive learning method for support vector machine (SVM) classifiers is proposed. We focus on a particular case of cost-sensitive problems, namely, classification with reject option. Standard learning algorithms, the one for SVMs included, are not cost-sensitive. In particular, they can not handle the reject option. However, we show that, under the framework of the structural risk minimisation induction principle, on which standard SVMs are based, the rejection region should be determined during the training phase of a classifier, by the learning algorithm. We apply this approach to develop a cost-sensitive SVM classifier, by following Vapnik's maximum margin method to the derivation of standard SVMs.