Effects of noise on the features of bispectrum

Higher-order Spectral (HOS) techniques can be used to detect deviations from linearity, stationarity or Guassianity in the signal. Most of the biomedical signals are non-linear, non-stationary, and non-Gaussian in nature. It is more useful to analyze them with HOS compared to the use of second-order statistics (power spectrum). There are some features in the bispectrum that are capable of differentiating between the signal and signal with noise. This study presents a technique of HOS to investigate the effect of noise on the features of third order statistics (bispectrum). The results show that the magnitudes of the bispectrum are consistently changing as a function of the amount of noise. In addition, these features can be extracted from the speech signal to compare with the respective behavioral responses, and thus a new metric to assess speech intelligibility and quality can be developed.