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An iterative, residual-based approach to unsupervised musical source separation in single-channel mixtures

机译:在单通道混合中用于无监督音源分离的迭代,基于残差的方法

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摘要

This thesis concentrates on a major problem within audio signal processing, the separation of source signals from musical mixtures when only a single mixture channel is available. Source separation is the process by which signals that correspond to distinct sources are identified in a signal mixture and extracted from it. Producing multiple entities from a single one is an extremely underdetermined task, so additional prior information can assist in setting appropriate constraints on the solution set. The approach proposed uses prior information such that: (1) it can potentially be applied successfully to a large variety of musical mixtures, and (2) it requires minimal user intervention and no prior learning/training procedures (i.e., it is an unsupervised process). This system can be useful for applications such as remixing, creative effects, restoration and for archiving musical material for internet delivery, amongst others. Here, specific priors include that the signal contains detectable musical events, with characteristic partial structures, often assumed to be harmonic. The harmonicity cue is incorporated by employing an adapted and extended frame-based multiF0 estimator for identifying the sources. This acts as a front-end to a source estimation and extraction stage. Further, an iterative procedure is introduced between the two stages, enabling improved performance via increased adaptivity to signal content: this novel approach becomes possible by exploiting a residual signal. Experimental results show that the proposed residual-based method achieves better average performance compared to alternative methods in terms of source separation and multiF0 estimation on a range of mixtures of varying complexity. Unmodelled content of the separated mixture will appear in the residual, which can be exploited further. In particular, a novel onset detection technique is proposed that works entirely with the residual. Considering its simplicity, the technique shows promising results compared to two existing methods that do not use the residual.
机译:本文着重于音频信号处理中的一个主要问题,即当只有单个混合通道可用时,将源信号与音乐混合音分开。源分离是一种过程,通过该过程可以识别信号混合物中与不同信号源相对应的信号并从中提取信号。从单个实体生产多个实体是一项非常不确定的任务,因此,其他先验信息可以帮助在解决方案集上设置适当的约束。建议的方法使用先验信息,以便:(1)可以成功地将其应用于多种音乐合辑,并且(2)它需要最少的用户干预,并且不需要事先的学习/培训程序(即,这是一个无监督的过程) )。该系统可用于诸如混音,创意效果,恢复以及存档音乐资料以进行互联网交付等应用。在此,特定的先验包括信号包含可检测的音乐事件,以及特征性的部分结构,通常被认为是谐波。谐波提示通过采用适应性和扩展的基于帧的multiF0估计器来识别源而被合并。这充当源估计和提取阶段的前端。此外,在两个阶段之间引入了一个迭代过程,从而通过增加对信号内容的适应性来提高性能:通过利用残留信号,这种新颖的方法成为可能。实验结果表明,与其他方法相比,基于残差的方法在不同复杂度范围内的源分离和multiF0估计方面具有更好的平均性能。分离出的混合物中未建模的含量会出现在残留物中,可以进一步利用。特别地,提出了一种新的开始检测技术,其完全与残差一起工作。考虑到其简单性,与不使用残差的两种现有方法相比,该技术显示出令人鼓舞的结果。

著录项

  • 作者

    Siamantas Georgios;

  • 作者单位
  • 年度 2009
  • 总页数
  • 原文格式 PDF
  • 正文语种 English
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