Abstract
With the growth of Internet, a large amount of music data is available for many music-related applications. It is almost impractical for these applications to satisfy the user requirements manually. To provide efficient services within these applications, the techniques developed for automatic music analysis, recommendation, and retrieval are urgently necessary. In this paper, we consider the applications of interactive music tutorials and distance education at music school. In the two applications, we need to integrate several techniques to achieve the educational purposes. This demand motives us to develop the advanced techniques for the performances of music services. In the area of music analysis, the music structure usually needs to be analyzed manually by experts, which is time-consuming and impractical. Therefore, we propose an approach for automatic music segmentation to extract the phrases and sentences of the musical structure. In addition to the rhythmic features, the melodic shape is first-ever used to improve the effectiveness of the music segmentation. Concerning a large number of music objects available in the databases, the systems that provide the services for users to look for their favorite music objects are urgently needed. One of the most important services for the users to escape from this information-overloading problem is the recommendation service. Due to the complex semantics of the music objects and the difficult derivation of user interests and behaviors, we propose an alternative way of music recommendation, which overcomes the limitations of the previous works. The music objects are first grouped based on the automatically extracted features. Moreover, the user access histories are analyzed to derive the profiles of user interests and behaviors for user grouping. The content-based, collaborative, and statistics-based recommendation methods are proposed based on the favorite degrees of the users to the music groups, and the user groups they belong to. Many interesting applications based on music streams, such as interactive music tutorials, distance music education, and similar theme searching, make the research of content-based retrieval over music streams much important. Therefore, we consider multiple queries with error tolerances over music streams and address the issue of approximate matching in this environment. To satisfy this demand, we propose a novel approach to continuously process multiple queries over the music streams for finding all the music segments that are similar to the queries. Our approach is based on the concept of n-grams and two mechanisms are designed to reduce the heavy computation of approximate matching. One mechanism uses the clustering of query n-grams to prune the query n-grams that are irrelevant to the incoming data n-gram. The other mechanism records the data n-gram that matches a query n-gram as a partial answer and incrementally merges the partial answers of the same query. A series of experiments are performed to demonstrate the effectiveness and efficiency of our approaches by comparing with other related works.