A comparison between Fourier and biological auditory based time-frequency distributions, applied to the speech signals

Time-Frequency Distributions (TFDs) are used to analyze signals whose spectral content varies along the time. Maybe the most popular TFD in the speech area is the spectrogram, derived from the Short-Time Fourier Transform. Recently a new method of analysis, called Wavelet Transform has become very useful in this area together with its respective TFD, the scalogram. Since the performance of the scalogram depends on the basic analyzing wavelet, each new wavelet results in a new time-frequency distribution. In this sense, a wavelet inspired in the biological auditory pathway can result in a scalogram that brings interesting characteristics for the analysis of multicomponent signals, like speech signal. The behavior of these two distributions is studied in this paper using the correlative domain. Results for conventional test signals, as well as for speech signals will be presented, showing that the scalogram derived from a biological auditory wavelet gives better results than the classical spectrogram