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This chapter will introduce the three research fields mentioned above. For each fields, we briefly review the attempts and take one of them as an example. Then we describe the mechanism, and the performance of each example to show the effectiveness of these techniques. And at last, we will show a music archive management system realized by us which utilizes the techniques described in this chapter.
Repetition detection is a fundamental issue for music thumbnailing and summarization. In this paper, we propose a new feature, called chroma histogram, which enables us to find out repetitive segments from popular songs accurately and quickly. The feature is robust to tempo variation, because sequential information is removed during the process. The low dimensional feature guarantees a very low computational...
This paper presents an approach to learn a better music similarity measure and presents an application to music playlist generation. Different from previous work, in our approach, automatically detected music attributes are used to represent each song. A set of kernels is employed in similarity measure, with each kernel measuring on a subset of music attributes and having a different importance weight...
Automatic evaluation of perceptual similarity is crucial for music retrieval. However, previous works mainly focused on the similarity of timbre and rhythm but not the musical pattern of a song, such as melody and chord. In this paper, we propose a new feature, chroma histogram, to summarize the musical pattern and use a transposition-invariant matching method to compare two chroma histograms. Experiment...
Vocal part detection, which plays an important role in music information retrieval, is still a tough task so far. Previous works focused on short time features, which cannot capture some essential long term characteristics of singing. In this paper, we propose a Dynamic Time Warping based unsupervised segmentation algorithm to divide a pop song into homogeneous segments, which contain either vocal...
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