Tomic Stefan T, Janata Petr
Center for Mind and Brain, University of California, Davis, California 95618, USA.
J Acoust Soc Am. 2008 Dec;124(6):4024-41. doi: 10.1121/1.3006382.
Current models for capturing metric structure of recordings of music are concerned primarily with the task of tempo and beat estimation. Even though these models have the potential for extracting other metric and rhythmic information, this potential has not been realized. In this paper, a model for describing the general metric structure of audio signals and behavioral data is presented. This model employs reson filters, rather than the comb filters used in earlier models. The oscillatory nature of reson filters is investigated, as they may be better suited for extracting multiple metric levels in the onset patterns of acoustic signals. The model is tested with several types of sequences of Dirac impulses as inputs, in order to investigate the model's sensitivity to timing variations and accent structure. The model's responses to natural stimuli are illustrated, both for excerpts of recorded music from a large database utilized by tempo-estimation models, and sequences of taps from a bimanual tapping task. Finally, the relationship of the model to several other beat-finding and rhythm models is discussed, and several applications and extensions for the model are suggested.
当前用于捕捉音乐录音的节拍结构的模型主要关注节拍和节奏估计任务。尽管这些模型有潜力提取其他节拍和节奏信息，但这种潜力尚未得到实现。本文提出了一种用于描述音频信号和行为数据的一般节拍结构的模型。该模型采用谐振滤波器，而不是早期模型中使用的梳状滤波器。研究了谐振滤波器的振荡特性，因为它们可能更适合在声学信号的起始模式中提取多个节拍层次。该模型以几种狄拉克脉冲序列作为输入进行测试，以研究模型对时间变化和重音结构的敏感性。展示了该模型对自然刺激的响应，包括节拍估计模型使用的大型数据库中录制音乐的片段以及双手敲击任务的敲击序列。最后，讨论了该模型与其他几种节拍查找和节奏模型的关系，并提出了该模型的几个应用和扩展。
