Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel algorithm for pitch frequency (PF) estimation from non-stationary noise-corrupted speech observations is presented in this paper based on both spectral pre-processing and temporal representation. A modified por spectral subtraction based de-noising scheme that allows tracking the time-variation of the underlying non-stationary noise is put forward to enhance speech prior to PF estimation. The de-noised speech is then utilized to propose a squared difference function of the Linear Prediction (LP) residual which is expected to reveal more prominent dips at integral multiples of the pitch period compared to that revealed by the LP residual. The dips at different pitch-harmonic locations are added and ighted by a periodicity dependent ighting factor for every possible pitch period thus yielding a ighted and harmonically summed temporal function which is globally minimized to extract the desired PF. Simulation results using the Keele database show the superior efficacy of the proposed method in the presence of a multi-talker babble noise relative to some of the existing methods.
['Celia Shahnaz', 'W.-P. Zhu', 'M.O. Ahmad']
I am writing a research paper on a idea A spectro-temporal algorithm for pitch frequency estimation from noisy observations
[{"from": "human", "value": "I am writing a research paper on a idea A spectro-temporal algorithm for pitch frequency estimation from noisy observations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel algorithm for pitch frequency (PF) estimation from non-stationary noise-corrupted speech observations is presented in this paper based on both spectral pre-processing and temporal representation. A modified por spectral subtraction based de-noising scheme that allows tracking the time-variation of the underlying non-stationary noise is put forward to enhance speech prior to PF estimation. The de-noised speech is then utilized to propose a squared difference function of the Linear Prediction (LP) residual which is expected to reveal more prominent dips at integral multiples of the pitch period compared to that revealed by the LP residual. The dips at different pitch-harmonic locations are added and ighted by a periodicity dependent ighting factor for every possible pitch period thus yielding a ighted and harmonically summed temporal function which is globally minimized to extract the desired PF. Simulation results using the Keele database show the superior efficacy of the proposed method in the presence of a multi-talker babble noise relative to some of the existing methods."}]