Deriving Musical Structures from Signal Analysis for Music Audio Summary Generation: "Sequence" and "State" Approach
Deriving Musical Structures from Signal Analysis for Music Audio Summary Generation: "Sequence" and "State" Approach
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从信号分析导出音乐结构以生成音乐音频摘要：“序列”和“状态”方法
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发表时间：
2003
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通讯作者：
Geoffroy Peeters
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作者：
Geoffroy Peeters
在本文中，我们研究了直接从信号分析中推导音乐结构的方法，目的是生成视觉和音频摘要。从音频信号中，我们首先得到特征-静态特征（MFCC，色谱图）或提出的动态特征。然后研究了两种方法，以便自动推导出一段音乐的结构。序列方法将音频信号视为事件序列的重复。提出了一种基于二维结构滤波和模式匹配的特征相似性矩阵序列提取算法。状态方法将音频信号视为一系列状态。由于人工分割和分组在随后的听证会上表现更好，因此本文采用了一种将时间分割和无监督学习方法相结合的多通道方法来遵循这种自然方法。序列和状态表示都用于使用各种技术创建音频摘要。
In this paper, we investigate the derivation of musical structures directly from signal analysis with the aim of generating visual and audio summaries. From the audio signal, we first derive features – static features (MFCC, chromagram) or proposed dynamic features. Two approaches are then studied in order to derive automatically the structure of a piece of music. The sequence approach considers the audio signal as a repetition of sequences of events. Sequences are derived from the similarity matrix of the features by a proposed algorithm based on a 2D structuring filter and pattern matching. The state approach considers the audio signal as a succession of states. Since human segmentation and grouping performs better upon subsequent hearings, this natural approach is followed here using a proposed multi-pass approach combining time segmentation and unsupervised learning methods. Both sequence and state representations are used for the creation of an audio summary using various techniques.