A SVM active learning method based on confidence, KNN and diversity

Audio is an important part of multimedia, and it has many useful applications in real life. Audio event classification is a key technology in audio management and application. Supervised audio event classification requires labeling large amounts of samples, while manual labeling is a very time-consuming work. In this paper we propose SVM_CKNND, an active learning method for SVM classifier, to deal with the labeling problem in audio event classification. For SVM_CKNND, in each iteration, first, a low-confidence region is delimited; then based on KNN, the samples that are more likely to be on the true class boundary are taken as the informative ones; finally, redundancy that exists in the informative samples is reduced to further decrease manual labeling workload. Experimental results show that SVM_CKNND performs better than another two SVM active learning algorithms, especially in classifying small-sample audio events.