1. A method of generating advanced feature discrimination vectors (AFDVs) representing sounds forming at least part of an input audio signal, the method comprising: taking a plurality of samples of the input audio signal, each of the samples being a portion of the input audio signal as it evolves over a window of predetermined time; for each sample of the audio signal taken: performing a signal analysis on the sample to extract one or more high resolution oscillator peaks therefrom, the extracted oscillator peaks forming a spectral representation of the sample; renormalizing the extracted oscillator peaks to eliminate variations in the fundamental frequency and time duration for each sample occurring over the window; normalizing the power of the renormalized extracted oscillator peaks; and forming the renormalized and power normalized extracted oscillator peaks into an AFDV for the sample and comparing the AFDV for the sample to at least one AFDV representing a known speech sound; wherein the renormalizing further comprises: for each of the samples of the audio signal analyzed: identifying a harmonically related group of the extracted oscillator peaks, each oscillator peak of the identified group being an approximately integer multiple of a fundamental frequency determined with a high resolution; and shifting the oscillator peaks to a periodic sequence of frequency bins beginning with an integer numbered bin for the fundamental frequency, and placing harmonics in multiples of the integer numbered bin, while maintaining the relative ordering of the identified oscillator peaks from the spectral representation of the windowed sample.
1. A method of generating advanced feature discrimination vectors (AFDVs) representing sounds forming at least part of an input audio signal, the method comprising: taking a plurality of samples of the input audio signal, each of the samples being a portion of the input audio signal as it evolves over a window of predetermined time; for each sample of the audio signal taken: performing a signal analysis on the sample to extract one or more high resolution oscillator peaks therefrom, the extracted oscillator peaks forming a spectral representation of the sample; renormalizing the extracted oscillator peaks to eliminate variations in the fundamental frequency and time duration for each sample occurring over the window; normalizing the power of the renormalized extracted oscillator peaks; and forming the renormalized and power normalized extracted oscillator peaks into an AFDV for the sample and comparing the AFDV for the sample to at least one AFDV representing a known speech sound; wherein the renormalizing further comprises: for each of the samples of the audio signal analyzed: identifying a harmonically related group of the extracted oscillator peaks, each oscillator peak of the identified group being an approximately integer multiple of a fundamental frequency determined with a high resolution; and shifting the oscillator peaks to a periodic sequence of frequency bins beginning with an integer numbered bin for the fundamental frequency, and placing harmonics in multiples of the integer numbered bin, while maintaining the relative ordering of the identified oscillator peaks from the spectral representation of the windowed sample. 16. The method of claim 1 wherein at least one of the extracted oscillator peaks is frequency modulated over the time of window.