Pattern Discovery Techniques for Music Audio
Pattern Discovery Techniques for Music Audio
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DOI：
10.1076/jnmr.32.2.153.16738
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发表时间：
2002-10
影响因子：
1.1
通讯作者：
R. Dannenberg;Ning Hu
中科院分区：
文献类型：
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作者：
R. Dannenberg;Ning Hu
人类听众能够通过感知音乐中的重复和其他关系来识别音乐中的结构。这项工作的目的是自动化的音乐分析任务。音乐是“解释”的嵌入式关系，特别是重复的片段或短语。这个过程中的步骤是将音频转录成具有相似性或距离度量的表示，搜索相似片段，形成相似片段的集群，并根据这些集群解释音乐。已经使用了几种预先存在的信号分析方法：单声道音高估计，色度（频谱）表示，以及和弦转录，然后进行谐波分析。此外，几种算法，搜索相似的段进行了说明。这些不同方法的经验表明，有许多方法可以从音乐音频中恢复结构。使用古典，爵士乐和摇滚乐提供的例子。
Human listeners are able to recognize structure in music through the perception of repetition and other relationships within a piece of music. This work aims to automate the task of music analysis. Music is “explained” in terms of embedded relationships, especially repetition of segments or phrases. The steps in this process are the transcription of audio into a representation with a similarity or distance metric, the search for similar segments, forming clusters of similar segments, and explaining music in terms of these clusters. Several pre-existing signal analysis methods have been used: monophonic pitch estimation, chroma (spectral) representation, and polyphonic transcription followed by harmonic analysis. Also, several algorithms that search for similar segments are described. Experience with these various approaches suggests that there are many ways to recover structure from music audio. Examples are offered using classical, jazz, and rock music.