Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present two novel algorithms for optimal MMSE estimation of the speech spectral amplitude, assuming it has been corrupted by additive and uncorrelated noise. The noise DFT coefficients are modelled as Gaussian random variables, while the speech spectral amplitude is modelled using either a Chi or a Gamma distribution. The influence of the priors' shape parameter is investigated. Results from simulations that demonstrate the performance of the proposed algorithms for different noise types and SNRs, as ll as a comparison with previously developed MAP estimators are presented.
['Ioannis Andrianakis', 'Paul R. White']
I am writing a research paper on a idea Mmse Speech Spectral Amplitude Estimators With Chi and Gamma Speech Priors
[{"from": "human", "value": "I am writing a research paper on a idea Mmse Speech Spectral Amplitude Estimators With Chi and Gamma Speech Priors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present two novel algorithms for optimal MMSE estimation of the speech spectral amplitude, assuming it has been corrupted by additive and uncorrelated noise. The noise DFT coefficients are modelled as Gaussian random variables, while the speech spectral amplitude is modelled using either a Chi or a Gamma distribution. The influence of the priors' shape parameter is investigated. Results from simulations that demonstrate the performance of the proposed algorithms for different noise types and SNRs, as ll as a comparison with previously developed MAP estimators are presented."}]