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  • 标题:Decoding of Envelope vs. Fundamental Frequency During Complex Auditory Stream Segregation
  • 本地全文:下载
  • 作者:Keelin M. Greenlaw ; Sebastian Puschmann ; Emily B. J. Coffey
  • 期刊名称:Neurobiology of Language
  • 电子版ISSN:2641-4368
  • 出版年度:2020
  • 卷号:1
  • 期号:3
  • 页码:268-287
  • DOI:10.1162/nol_a_00013
  • 语种:English
  • 出版社:The MIT Press
  • 摘要:AbstractHearing-in-noise perception is a challenging task that is critical to human function, but how the brain accomplishes it is not well understood. A candidate mechanism proposes that the neural representation of an attended auditory stream is enhanced relative to background sound via a combination of bottom-up and top-down mechanisms. To date, few studies have compared neural representation and its task-related enhancement across frequency bands that carry different auditory information, such as a sound’s amplitude envelope (i.e., syllabic rate or rhythm; 1–9 Hz), and the fundamental frequency of periodic stimuli (i.e., pitch; >40 Hz). Furthermore, hearing-in-noise in the real world is frequently both messier and richer than the majority of tasks used in its study. In the present study, we use continuous sound excerpts that simultaneously offer predictive, visual, and spatial cues to help listeners separate the target from four acoustically similar simultaneously presented sound streams. We show that while both lower and higher frequency information about the entire sound stream is represented in the brain’s response, the to-be-attended sound stream is strongly enhanced only in the slower, lower frequency sound representations. These results are consistent with the hypothesis that attended sound representations are strengthened progressively at higher level, later processing stages, and that the interaction of multiple brain systems can aid in this process. Our findings contribute to our understanding of auditory stream separation in difficult, naturalistic listening conditions and demonstrate that pitch and envelope information can be decoded from single-channel EEG data.
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