Exploring composite acoustic features for efficient music similarity query

Music similarity query based on acoustic content is becoming important with the ever-increasing growth of the music information from emerging applications such as digital libraries and WWW. However, relative techniques are still in their infancy and much less than satisfactory. In this paper, we present a novel index structure, called Composite Feature tree, CF-tree, to facilitate efficient content-based music search adopting multiple musical features. Before constructing the tree structure, we use PCA to transform the extracted features into a new space sorted by the importance of acoustic features. The CF-tree is a balanced multi-way tree structure where each level represents the data space at different dimensionalities. The PCA transformed data and reduced dimensions in the upper levels can alleviate suffering from dimensionality curse. To accurately mimic human perception, an extension, named CF+-tree, is proposed, which further applies multivariable regression to determine the weight of each individual feature. We conduct extensive experiments to evaluate the proposed structures against state-of-art techniques. The experimental results demonstrate superiority of our technique.

Academic journal publishing: Desiderata for the digital age

The consumption of academic journals has radically changed over the past decade, explains the author. While there has been an exponential rise in published scholarship, spiralling costs for commercial journals have caused cutbacks in subscriptions to academic journals by institutional libraries and raised calls for free online access to unpublished work that scholars have produced. The rise of the Internet has facilitated a concomitant growth in online scholarship. What, asks the author, are the promises on online scholarship?