Earticle

With the explosive growth of digital music, content-based music information retrieval especially query by humming / singing have been attracting more and more attention and are becoming popular research topics over the past decade. Although query by humming / singing can provide natural and intuitive way to search music, retrieval system still confronts many issues such as key modulation, tempo change, note insertion, deletion or substitution which are caused by users and query transcription respectively. In this paper, we propose a novel approach based on fault tolerance and recursive segmentation to solve above problems. Music melodies in database are represented with specified manner and indexed using inverted index method. Query melody is segmented into phrases recursively with musical dictionary firstly. Then improved edit distance, pitch deviation and overall bias are employed to measure the similarity between phrases and indexed entries. Experimental results reveal that proposed approach can achieve high recall for music retrieval.

In this short paper we present OCL knowledge representation for interface constraints using a framework known as MDKR. The semantics of OCL [1,2] are visualised and represented in the form of set-relationship diagram and is finally embedded with knowledge semantics. Using these semantics we have developed a formal correctness notation [3] for relationship between interfaces of web pages.

One of the central problems in the study of Support vector machine (SVM) is kernel selection, that’s based essentially on the problem of choosing a kernel function for a particular task and dataset. By contradiction to other machine learning algorithms, SVM focuses on maximizing the generalisation ability, which depends on the empirical risk and the complexity of the machine. In the following paper, we considered the problem of kernel selection of SVMs classifiers to achieve performance on text-independent speaker identification using the TIMIT corpus. We were focused on SVM trained using linear, polynomial and Radial Basic Function (RBF) kernels. A preliminary study has been made between SVM using the best choice of kernel and three other popular learning algorithms, namely Naive Bayes (NB), decision tree C4.5 and Multi Layer Perceptron (MLP). Results had revealed that SVM trained using polynomial kernel is the best choice for dealing with speaker identification tasks and that SVM is the best choice when compared to other algorithms.