Recognition and Visualization of Music Sequences Using Self-organizing Feature Maps

机译：使用自组织特征图对音乐序列进行识别和可视化

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摘要

Music consists of sequences, e.g., melodic, rhythmic or harmonic passages. The analysis and automatic discovery of sequences in music has an important part to play in different applications, e.g., intelligent fast-forward to new parts of a song, assisting tools in music composition, or automated spinning of records. In this paper we introduce a method for the automatic discovery of sequences in a song based on self-organizing maps and approximate motif search. In a preprocessing step high-dimensional music feature vectors are extracted on the level of bars, and translated into low-dimensional symbols, i.e., neurons of a self-organizing feature map. We use this quantization of bars for visualization of the song structure and for the recognition of motifs. An experimental analysis on real music data and a comparison to human analysis complements the results.

机译：音乐由序列组成，例如旋律，节奏或和声乐段。音乐中序列的分析和自动发现在不同的应用程序中扮演着重要的角色，例如，智能快进歌曲的新部分，辅助音乐创作工具或自动旋转唱片。在本文中，我们介绍了一种基于自组织图和近似主题搜索自动发现歌曲序列的方法。在预处理步骤中，在小节的水平上提取高维音乐特征向量，并将其转换为低维符号，即自组织特征图的神经元。我们使用这种条形量化来显示歌曲结构和识别图案。对真实音乐数据的实验分析以及与人为分析的比较补充了结果。

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《KI 2010: Advances in artificial intelligence》|2010年|p.160-167|共8页
会议地点 Karlsruhe(DE);Karlsruhe(DE)
作者
Tobias Hein; Oliver Kramer;
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作者单位

Department of Computer Science, Technische Universitat Dortmund,44227 Dortmund, Germany;

International Computer Science Institute,Berkeley, CA 94704, USA;

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原文格式 PDF
正文语种 eng
中图分类人工智能理论;
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3. Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps [J] . Xingfan ZHOU, Zengling YANG, Longjian CHEN, 农业科学与工程前沿：英文版 . 2016,第002期

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4. Recognition and Visualization of Music Sequences Using Self-organizing Feature Maps [C] . Tobias Hein, Oliver Kramer Annual German Conference on Artificial Intelligence . 2010

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6. Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps [O] . Peter Meinicke, Thomas Lingner, Alexander Kaever, 2008

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7. HAND TRAJECTORY BASED GESTURE RECOGNITION USING SELF-ORGANIZING FEATURE MAPS AND MARKOV MODELS [O] . George Caridakis, Kostas Karpouzis, Christos Pateritsas, 2009

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机译：利用自组织映射的扩展对大蛋白质序列数据库进行聚类和可视化

Recognition and Visualization of Music Sequences Using Self-organizing Feature Maps

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