Gaussian Prior Stimulus Generation¶

This is a stimulus generation class in which the number of filled bins is selected from a Gaussian distribution with known mean and variance parameters.

Unique Properties¶

This stimulus generation class has two properties in addition to those inhereted from the Abstract and Abstract Binned classes. Defaults:

- n_bins_filled_mean = 20 % Mean of the Gaussian from which number of filled bins is selected.
- n_bins_filled_var = 1 % Variance of the Gaussian from which number of filled bins is selected.

generate_stimulus¶

[stim, Fs, spect, binned_repr, frequency_vector] = generate_stimulus(self)

Generate a stimulus vector of length self.nfft+1. the bin amplitudes are self.unfilled_dB for an unfilled bin and self.filled_dB for a filled bin. Filled bins are chosen uniformly from unfilled bins, one at a time. The total number of bins-to-be-filled is chosen from a Gaussian distribution.

OUTPUTS:

stim: self.nfft + 1 x 1 numerical vector, the stimulus waveform,

Fs: 1x1 numerical scalar, the sample rate in Hz.

spect: self.nfft / 2 x 1 numerical vector, the half-spectrum, in dB.

binned_repr: self.n_bins x 1 numerical vector, the binned representation.

frequency_vector: self.nfft / 2 x 1 numerical vector, the frequencies associated with the spectrum, in Hz.

Class Properties Used:

- n_bins
- n_bins_filled_mean
- n_bins_filled_var
- unfilled_dB
- filled_dB