论文标题
使用几何相似性度量缩放和压缩旋律
Scaling and compressing melodies using geometric similarity measures
论文作者
论文摘要
旋律相似性测量在音乐信息检索中至关重要。在本文中，我们使用几何匹配技术来测量两种旋律之间的相似性。我们将音乐表示为欧几里得平面中的一组点或水平线段集，并提出了有效的算法，以实现两种关于旋律操作的优化问题；线性缩放和音频压缩。在缩放问题中，向前缩放了传入的查询旋律，直到查询和参考旋律之间的相似性度量最小化。压缩问题要求给定旋律的音符子集，以便将所选音符和参考旋律之间的匹配成本最小化。
Melodic similarity measurement is of key importance in music information retrieval. In this paper, we use geometric matching techniques to measure the similarity between two melodies. We represent music as sets of points or sets of horizontal line segments in the Euclidean plane and propose efficient algorithms for optimization problems inspired in two operations on melodies; linear scaling and audio compression. In the scaling problem, an incoming query melody is scaled forward until the similarity measure between the query and a reference melody is minimized. The compression problem asks for a subset of notes of a given melody such that the matching cost between the selected notes and the reference melody is minimized.
