Spectral Subtraction Based Weighted Function for Pitch Extraction in Noisy Speech

Many speech-related works employ the pitch period as a crucial component. Speech signals are typically collected in challenging noisy settings for real-world projects. Therefore, it is now more important than ever for the algorithm to be noise resistant in order to estimate pitch accurately. However, when dealing with noisy speech files at a low signal-to-noise ratio (SNR) value, many state-of-the-art algorithms are unable to produce satisfactory results. In this work, a new noise-resistant pitch estimation algorithm based on spectral subtraction is presented, which uses a weighted function to lessen the impact of the vocal tract effect. Furthermore, to enhance the correlation between the pitch estimates and smoothen the pitch contours, we employ a weighted function that combines the spectral subtraction-based technique as the numerator and the circular average magnitude difference function (CAMDF) as the denominator. We have utilized two noisy speech databases using seven different kinds of recorded ambient noise, and we evaluated our system against three cutting-edge methods. The suggested method lowers the Gross Pitch Error (GPE) rate at practically all SNRs in white noise and performs best on the NTT and KEELE databases.