声学
带阻滤波器
噪音(视频)
带宽(计算)
信号(编程语言)
滤波器设计
滤波器(信号处理)
频率响应
听觉系统
低通滤波器
计算机科学
声压
物理
降噪
数学
电信
计算机视觉
图像(数学)
电气工程
工程类
人工智能
程序设计语言
听力学
医学
作者
Brian R. Glasberg,Brian C. J. Moore
标识
DOI:10.1016/0378-5955(90)90170-t
摘要
A well established method for estimating the shape of the auditory filter is based on the measurement of the threshold of a sinusoidal signal in a notched-noise masker, as a function of notch width. To measure the asymmetry of the filter, the notch has to be placed both symmetrically and asymmetrically about the signal frequency. In previous work several simplifying assumptions and approximations were made in deriving auditory filter shapes from the data. In this paper we describe modifications to the fitting procedure which allow more accurate derivations. These include: 1) taking into account changes in filter bandwidth with centre frequency when allowing for the effects of off-frequency listening; 2) correcting for the non-flat frequency response of the earphone; 3) correcting for the transmission characteristics of the outer and middle ear; 4) limiting the amount by which the centre frequency of the filter can shift in order to maximise the signal-to-masker ratio. In many cases, these modifications result in only small changes to the derived filter shape. However, at very high and very low centre frequencies and for hearing-impaired subjects the differences can be substantial. It is also shown that filter shapes derived from data where the notch is always placed symmetrically about the signal frequency can be seriously in error when the underlying filter is markedly asymmetric. New formulae are suggested describing the variation of the auditory filter with frequency and level. The implication of the results for the calculation of excitation patterns are discussed and a modified procedure is proposed. The appendix list FORTRAN computer programs for deriving auditory filter shapes from notched-noise data and for calculating excitation patterns. The first program can readily be modified so as to derive auditory filter shapes from data obtained with other types of maskers, such as rippled noise.
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