Eggermont J J, Smith G M
Department of Psychology, University of Calgary, Alberta, Canada.
J Acoust Soc Am. 1990 Jan;87(1):246-59. doi: 10.1121/1.399291.
Because of their dynamic properties, most sounds can best be characterized in the combined frequency-time (FT) domain. Powerful frequency-time characterizations are the Wigner distribution function (WDF) and the Rihacek energy density function (RDF). In the present paper several new concepts are introduced such as using the WDF to characterize the tuning of auditory neurons under wideband noise stimulation and a new method to quantify phase lock of auditory neurons to a wideband noise. No appreciable differences were found between the WDF and RDF in narrow-band signal representations. However, the differences between the WDF and RDF increase as the bandwidth of the signal increases. When signals are buried in uncorrelated background noise, the average FT function of these signals may be obtained through averaging the FT functions for each signal plus noise segment. The WDF takes at least a factor 2 more in time to compute than the RDF. The FT functions can be used to characterize (linear) filters by averaging FT functions of input-noise segments that precede threshold crossings of the filter's output signal. Both the WDF and the RDF were used to characterize auditory neurons from the midbrain in anurans; the WDF always had a smaller bandwidth than the RDF. By comparing the spectrum of the reverse correlation function and the average spectrum of the noise segments preceding the spikes, a quantification of the amount of phase lock of the auditory neuron to the noise is obtained.
由于其动态特性，大多数声音最好在联合频率 - 时间（FT）域中进行表征。强大的频率 - 时间表征方法是维格纳分布函数（WDF）和里哈切克能量密度函数（RDF）。在本文中，引入了几个新概念，例如使用WDF来表征宽带噪声刺激下听觉神经元的调谐，以及一种量化听觉神经元对宽带噪声的锁相的新方法。在窄带信号表示中，未发现WDF和RDF之间有明显差异。然而，随着信号带宽的增加，WDF和RDF之间的差异会增大。当信号被埋在不相关的背景噪声中时，这些信号的平均FT函数可以通过对每个信号加噪声段的FT函数进行平均来获得。计算WDF所需的时间至少比RDF多一倍。FT函数可用于通过对滤波器输出信号阈值交叉之前的输入噪声段的FT函数进行平均来表征（线性）滤波器。WDF和RDF都被用于表征无尾两栖动物中脑的听觉神经元；WDF的带宽总是比RDF小。通过比较反向相关函数的频谱和尖峰之前噪声段的平均频谱，可以获得听觉神经元对噪声的锁相量的量化结果。
