Large scale music information retrieval by semantic tags

Abstract

Model-driven and Data-driven methods are two widely adopted paradigms in Query by Description (QBD) music search engines. Model-driven methods attempt to learn the mapping between low-level features and high-level music semantic meaningful tags, the performance of which are generally affected by the well-known semantic gap. On the other hand, Data-driven approaches rely on the large amount of noisy social tags annotated by users. In this thesis, we focus on how to design a novel Model-driven method and combine two approaches to improve the performance of music search engines. With the increasing number of digital tracks appear on the Internet, our system is also designed for large-scale deployment, on the order of millions of objects. For processing large-scale music data sets, we design parallel algorithms based on the MapReduce framework to perform large-scale music content and social tag analysis, train a model, and compute tag similarity. We evaluate our methods on CAL-500 and a large-scale data set (N = 77, 448 songs) generated by crawling Youtube and Last.fm. Our results indicate that our proposed method is both effective for generating relevant tags and efficient at scalable processing. Besides, we also have implemented a web-based prototype music retrieval system as a demonstration.