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“原始”听觉流分离中音调拓扑、适应、调制调谐和时间相干性的影响

Effects of tonotopicity, adaptation, modulation tuning, and temporal coherence in "primitive" auditory stream segregation.

作者信息

Christiansen Simon Krogholt, Jepsen Morten Løve, Dau Torsten

机构信息

Centre for Applied Hearing Research, Technical University of Denmark, DK-2800 Lyngby, Denmark.

Widex A/S, DK-3560 Lynge, Denmark.

出版信息

J Acoust Soc Am. 2014 Jan;135(1):323-33. doi: 10.1121/1.4845675.

Abstract

The perceptual organization of two-tone sequences into auditory streams was investigated using a modeling framework consisting of an auditory pre-processing front end [Dau et al., J. Acoust. Soc. Am. 102, 2892-2905 (1997)] combined with a temporal coherence-analysis back end [Elhilali et al., Neuron 61, 317-329 (2009)]. Two experimental paradigms were considered: (i) Stream segregation as a function of tone repetition time (TRT) and frequency separation (Δf) and (ii) grouping of distant spectral components based on onset/offset synchrony. The simulated and experimental results of the present study supported the hypothesis that forward masking enhances the ability to perceptually segregate spectrally close tone sequences. Furthermore, the modeling suggested that effects of neural adaptation and processing though modulation-frequency selective filters may enhance the sensitivity to onset asynchrony of spectral components, facilitating the listeners' ability to segregate temporally overlapping sounds into separate auditory objects. Overall, the modeling framework may be useful to study the contributions of bottom-up auditory features on "primitive" grouping, also in more complex acoustic scenarios than those considered here.

摘要

使用一个建模框架研究了双音序列在听觉流中的感知组织,该框架由一个听觉预处理前端[道等人,《美国声学学会杂志》102,2892 - 2905(1997)]与一个时间相干分析后端[埃尔希拉利等人,《神经元》61,317 - 329(2009)]组成。考虑了两种实验范式:(i)作为音调重复时间(TRT)和频率间隔(Δf)函数的流分离,以及(ii)基于起始/结束同步对远距离频谱成分的分组。本研究的模拟和实验结果支持了以下假设:前向掩蔽增强了在感知上分离频谱相近音调序列的能力。此外,建模表明神经适应和通过调制频率选择滤波器进行处理的效果可能会提高对频谱成分起始异步的敏感性,有助于听众将时间上重叠的声音分离成不同的听觉对象。总体而言,该建模框架可能有助于研究自下而上的听觉特征对“原始”分组的贡献,即使在比这里考虑的更复杂的声学场景中也是如此。

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