Enhancing Similarity Matrices for Music Audio Analysis
Enhancing Similarity Matrices for Music Audio Analysis
复制标题
增强音乐音频分析的相似性矩阵
DOI：
10.1109/icassp.2006.1661199
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
F. Kurth
中科院分区：
文献类型：
--
作者：
Meinard Müller;F. Kurth
相似性矩阵已经成为音乐音频分析的重要工具。然而，二次的时间和空间的复杂性，以及从这些矩阵中提取所需的结构信息的复杂性，往往是禁止在现实世界中的应用。在本文中，我们描述了一种基于两个概念的相似性矩阵的结构特性增强的方法：首先，我们引入了一类新的鲁棒性和可扩展的音频功能，吸收当地的时间变化。作为第二个贡献，我们然后将上下文信息纳入本地相似性度量。所得到的增强导致矩阵尺寸的显著减小，并且还简化了结构提取步骤。作为一个例子，我们勾勒出我们的技术的应用问题的音频摘要和音频同步，获得有效的和计算上可行的算法
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 applications. 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 example, we sketch the application of our techniques to the problems of audio summarization and audio synchronization, obtaining effective and computationally feasible algorithms