A Novel Method for Query-by-Humming Using Distance Space

Abstract

Signal of humming sound is the input which is important for the Query-by-Humming system. This input signal which has variable dimension depend on humming time interval will always affect the feature vector. It cannot be used with some classifiers, which require non-variable dimension of feature vector, such as Artificial Neural Network (ANN) or Support Vector Machine (SVM). Especially, SVM is good classifier and it might be appropriate for our work. Because of each signal of humming sound has variable dimension and length, this is the main problem which we would like to come up with the idea to solve it. We have an idea to create a new feature space that has the same dimension in order to use with SVM classifier. In this paper, we propose indirect feature, it is used distance between template and observation sequence for creating new feature vector. This technique can be briefly described: Firstly, templates are distributed in original feature space. When the observation sequence gets into this space, Dynamic Time Warping (DTW) will measure the distance between observation sequence and existing templates. These distance are used to get the new feature vector in new space, called distance space. In this way, all feature vectors are non-variable dimension therefore we used SVM and ANN classifier. The experimental results show that the new feature vector which is used by SVM classifier gives better results than ANN.