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Learning techniques for identifying vocal regions in music using the wavelet transformation, version 1.0.

机译：使用小波变换（版本1.0）识别音乐中人声区域的学习技术。

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摘要

In this research I present a machine learning method for the automatic detection of vocal regions in music. I employ the wavelet transformation to extract wavelet coefficients, from which I build feature sets capable of constructing a model that can distinguish between regions of a song that contain vocals and those that are purely instrumental. Singing voice detection is an important aspect of the broader field of Music Information Retrieval, and efficient vocal region detection facilitates further research in other areas such as a singing voice detection, genre classification and the management of large music databases. As such, it is important for researchers to accurately detect automatically which sections of music contain vocals and which do not. Previous methods that used features, such as the popular Mel-Frequency Cepstral Coefficients (MFCC), have several disadvantages when analyzing signals in the time-frequency domain that the wavelet transformation can overcome. The models constructed by using the wavelet transformation on a windowed music signal produce a classification accuracy of 86.66%, 11% higher than models built using MFCCs. Additionally, I show that applying a decision tree algorithm to the vocal region detection problem will produce a more accurate model when compared to other, more widely applied learning algorithms, such as Support Vector Machines.
机译：在这项研究中，我提出了一种用于自动检测音乐中声带的机器学习方法。我采用小波变换来提取小波系数，从中建立特征集，该特征集能够构建一个模型，该模型可以区分歌曲中包含人声的部分和纯粹是器乐性的部分。唱歌语音检测是音乐信息检索领域的重要方面，有效的人声区域检测有助于其他领域的进一步研究，例如唱歌语音检测，体裁分类和大型音乐数据库的管理。因此，对于研究人员而言，准确地自动检测音乐的哪些部分包含人声是非常重要的。当在时频域中分析小波变换可以克服的信号时，使用诸如流行的梅尔频率倒谱系数（MFCC）之类的功能的先前方法具有多个缺点。在窗口音乐信号上使用小波变换构造的模型产生86.66％的分类精度，比使用MFCC构造的模型高11％。此外，我证明，与其他更广泛应用的学习算法（例如支持向量机）相比，将决策树算法应用于人声区域检测问题将产生更准确的模型。

著录项

  • 作者

    Henry, Michael J.;

  • 作者单位

    Georgetown University.;

  • 授予单位 Georgetown University.;
  • 学科 Music.;Computer Science.;Artificial Intelligence.
  • 学位 M.S.
  • 年度 2011
  • 页码 112 p.
  • 总页数 112
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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