When a user cannot remember the title of a song, or its related details, the most direct and convenient method to search for the song is by humming a section of it. This search method is particularly important when a user does not have access to operate the audio device. The design methodology used in conventional search mechanisms that query by singing/humming, commonly emphasize signal processing or music comparison. The background of the user often influences the genres of the songs being searched, and this is an area of research seldom studied. In our study, we use the information from a user's search history, as well as the properties of genres common to users with similar backgrounds, to estimate the genre or style the current user may be interested in based on a probability calculation. The accuracy from querying by singing/humming is improved. Our method can be divided into two phases. In the first phase, we find the possible search results. This is similar to the conventional singing/humming query process. During the second phase, the musical preference of the user is utilized to rank the possible search results again. Songs that are most likely to be queried would be positioned at the front of the list in the search results. Through our experiments, significant improvement is demonstrated with our method.

In this paper, we describe the Ultima project which aims to construct a platform for evaluating various approaches of music information retrieval. Two kinds of approaches are adopted in this project. These approaches differ in various aspects, such as representations of music objects, index structures, and approximate query processing strategies. For a fair comparison, we propose a measurement of the retrieval effectiveness by recall-precision curves with a scaling factor adjustment. Finally, the performance study of the retrieval effectiveness based on various factors of these approaches is presented.