Musical Genre Classification of Audio Signals
Musical Genre Classification of Audio Signals音频信号的音乐流派分类
Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized
by the common characteristics shared by its members. These characteristics typically are related to the instrumentation,
rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content- based analysis of musical signals. In this paper, the automatic classification of audio signals into an hierarchy of musical genres is explored. More specifically, three feature sets for representing timbral texture, rhythmic co ntent and pitch content are proposed. The performance and relative importance of the proposed features is investigated by training statistical pattern recognition classifiers using real-world audio collections. Both whole file and real-time frame-based classification schemes are described. Using the proposed feature sets, classification of 61% for ten musical genres is achieved. This result is comparable to results reported for human musical genre classification.
音乐流派是人类创造的用来描述音乐作品的分类标签。一种音乐流派的特点是其成员所共有的特征。这些特征通常与音乐的乐器、节奏结构和和声内容有关。流派层次结构通常用于构建网络上的大量音乐集合。目前的音乐流派注释是手动执行的。在这一过程中，音乐类型自动分类可以帮助或代替人类用户，对音乐信息检索系统来说是一个有价值的补充。此外，自动音乐类型分类为开发和评估任何类型的基于内容的音乐信号分析的特征提供了一个框架。本文探讨了将音频信号自动分类为不同音乐流派的方法。更具体地说，提出了表示音色纹理、节奏内容和音高内容的三个特征集。通过使用真实世界的音频集合训练统计模式识别分类器来研究所建议特征的性能和相对重要性。描述了全文件和基于实时帧的分类方案。利用所提出的特征集，对10个音乐类型进行了61%的分类。这一结果可与人类音乐类型分类的结果相比较。
Index Terms—Audio classification, beat analysis, feature extraction,musical genre classification, wavelets.
索引术语-音频分类，节拍分析，特征提取，音乐体裁分类，小波。
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