首页> 外文会议>IEEE International Conference on Acoustics, Speech, and Signal Processing >UNSUPERVISED TRAINING OF DETECTION THRESHOLD FOR POLYPHONIC MUSICAL NOTE TRACKING BASED ON EVENT PERIODICITY
【24h】

UNSUPERVISED TRAINING OF DETECTION THRESHOLD FOR POLYPHONIC MUSICAL NOTE TRACKING BASED ON EVENT PERIODICITY

机译:基于事件周期的复音音符跟踪检测阈值无监督

获取原文

摘要

A common approach to the detection of simultaneous musical notes in an acoustic recording involves defining a function that yields activation levels for each candidate musical note over time. These levels tend to be high when the note is active and low when it is not. Therefore, by applying a simple threshold decision process, it is possible to decide whether each note is active or not at a given time. Such a threshold, in general, is hard to set and has no physical meaning. In this paper, it is shown that the rhythmic characteristic of the musical signal may be used to obtain a suitable threshold. The proposed method for obtaining the threshold is shown to have a greater generalization capability over different databases.
机译:在声学记录中检测同步音符的常见方法涉及定义一个函数,其随着时间的推移为每个候选音符产生激活水平。当音符处于活动状态和低时,这些级别往往很高。因此,通过应用简单的阈值决策过程,可以决定每个音符是否在给定时间处于活动状态。通常,这种阈值很难设置并且没有物理意义。在本文中,示出了音乐信号的节奏特性可以用于获得合适的阈值。所提出的用于获得阈值的方法被示出在不同的数据库中具有更大的概括能力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号