A New Approach to Fourier Synthesis With Application to Neural Encoding and Speech Classification

Abstract

We describe a novel means of representing signals by a Fourier decomposition consisting of complex sinusoids with unit amplitudes and zero phases. The only information necessary to reconstruct the signal from its Fourier components consists of the “place” information, which specifies the sinusoidal frequencies to include in the synthesis. This set of frequencies results in a nonuniform distribution of sinusoidal frequency components. As such, the approach provides a means of representing a signal by a set of zeros and ones, indicating an off-on condition for each frequency component. It is conjectured that this might help explain the mechanism of auditory and visual neural encoding of acoustic and visual stimuli, respectively. As an immediate application of the theory, a classification experiment is conducted which indicates that the proposed neural encoding is more robust to noise than traditional approaches.