English
资源说明：Similarity matrices have become an important tool in music audio
analysis. However, the quadratic time and space complexity as well
as the intricacy of extracting the desired structural information from
these matrices are often prohibitive with regard to real-world appli-
cations. In this paper, we describe an approach for enhancing the
structural properties of similarity matrices based on two concepts:
first, we introduce a new class of robust and scalable audio features
which absorb local temporal variations. As a second contribution,
we then incorporate contextual information into the local similarity
measure. The resulting enhancement leads to significant reduction in
matrix size and also eases the structure extraction step. As an exam-
ple, we sketch the application of our techniques to the problems of
audio summarization and audio synchronization, obtaining effective
and computationally feasible algorithms.
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