Abstract
Recently there has been a great deal of attention paid to the automatic prediction of tags for music and audio in general. Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web 2.0” recommender systems. There have been many attempts at automatically applying tags to audio for different purposes: database management, music recommendation, improved human-computer interfaces, estimating similarity among songs, and so on. Many published results show that this problem can be tackled using machine learning techniques, however, no method so far has been proven to be particularly suited to the task. First, it seems that no one has yet found an appropriate algorithm to solve this challenge. But second, the task definition itself is problematic. In an effort to better understand the task and also to help new researchers bring their insights to bear on this problem, this chapter provides a review of the state-of-the-art methods for addressing automatic tagging of audio. It is divided in the following sections: goal, framework, audio representation, labeled data, classification, evaluation, and future directions. Such a division helps understand the commonalities and strengths of the different methods that have been proposed.
Download an Excerpt by the Authors: Automatic Tagging of Audio: The State-of-the-Art
An appropriate algorithm?
Proposed … (Derived from Progressive Recursion (PR):
Tn (2) = (a, b) →
n = (a1, b1); n = (a2, b2) →
a* b = (a1 * b1) * 22n+ a2* b2 + (a1* b2 + a2* b1) * 2n or Tn(2n)= 4Tn(n+ n)
