Fundamental Frequency Extraction by Utilizing the Autocorrelation Based Spectral Subtraction Method in Noisy Speech

Abstract

This research suggests an efficient method that is better suited for speech processing applications to retrieve the accurate fundamental frequencies from speech in noisy conditions. For this objective, a fundamental frequency extraction algorithm that is tolerant to the non-stationary changes of the amplitude and frequency of the input signal is presented. Moreover, autocorrelation function is used as a pre-processor, which uses the power spectrum of the input signal to reduce the noise characteristics of the speech signals. In order to increase the accuracy of the fundamental frequency extraction, the focus has been on maintaining the speech harmonics in their original state and suppressing the noise elements involved in the noisy speech signal. The two stages that make up the suggested fundamental frequency extraction approach are producing the autocorrelation function of the speech signal as a pre-processor and applying it with the spectral subtraction method. As per the experiment results, the proposed technique appears to be better in noisy situations than other existing state-of-the-art methods.
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