With the rapid evolution of information techniques, the amount of data in various media tends to be increased explosively. Digital music exists everywhere and grows day by day. At the same time, various music applications accelerate the progress of information techniques very much. For instance, peer-to-peer went mainstream in the late 20 centuries because of music sharing. As a burgeoning field, there is a wide future for music data management and information retrieval in both theoretical research and real-life applications. Research results in this filed are far from satisfying people's needs for real applications. Thus, this paper aims at music data, exploits data management and statistical computing methods, and studies theory, techniques and methods of music data management, as well as content-based music information retrieval techniques in peer-to-peer environments. The main contributions of this paper are as follows:|
In the field of music data management, we propose data model, query language, storage structures, access methods, theme mining algorithms and query processing methods. In detail, a music data model is presented, which can not only express various complex hierarchical structures and their semantics of music data efficiently, but also support various music computing applications. A music data definition and manipulation language MuSQL is also given, which directly supports multifarious music operations. The structured storage policy is proposed to store various music data. N-gram inverted index structures are proposed for music applications, which can be established easily and can be implemented in a database system simply. Abstract indices (AbIx) in P2P environments are presented, including central AbIx in centralized P2P data systems, plain AbIx in distributed P2P data systems, and cell AbIx in structured P2P data systems. Abstract indices have many advantages, such as low system cost, improving the query processing speed, supporting high autonomy of peers and very frequent updates. They are also applicable to other media besides of music. The music theme mining algorithm CDM is proposed, whose performance is better than any other algorithm for the same purpose. A content-based music data query processing algorithm is presented, and two implementations of the algorithm, i.e. NestedLoop and SortMerge, are also given. Techniques on content-based approximate query processing are discussed. The algorithms to achieve the set of candidate peers is proposed in centralized, distributed and structured peer-to-peer data systems. The content-based approximate query processing methods are also given. It can be used to search as few peers as possible but get as many returns satisfying users' queries as possible. The proposed methods are also applicable to other distributed environments, such as Grid, Deep Web and so on.
In the field of music information retrieval, we present and analyze four schemes and retrieval algorithms for music information retrieval in peer-to-peer environments, and propose an implementation of PsPsC scheme. In detail, the content-based music information retrieval problem in peer-to-peer environments is proposed and four peer-to-peer schemes for content-based music information retrieval are presented. After evaluation of these themes on network load, retrieval time, system update and robustness in theoretical analysis and experimental comparisons, PsPsC scheme is found out to be the best one for approximate queries and PsC+ is best for exact queries. An implementation of PsPsC scheme is presented, including the feature-extracting method, the retrieval algorithm, the method to filter out the replica in the final results, and the system architecture. Some simulated experiments are made to show the efficiency of proposed algorithms.
The music data management system HIT-DML (Harbin Institute of Technology-Digital Music Library) has been designed and implemented on the basis of the above fundamental research harvest. It adopts a novel framework and is inherently based on database systems. Musical content data, feature data and meta-data are structurally stored in databases. Some music operations, such as feature extracting, are implemented within the database system, and database technologies are combined with multimedia technologies seamlessly. The advanced features of database systems, such as transaction processing, are utilized. It can retrieve and play music data based on content against different kinds of musical instruments. HIT-DML verifies the theoretical and practical significances of our proposed key techniques on management and retrieval of music data.