Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A method for applying Kalman filtering to speech signals corrupted by colored noise is presented. Both speech and colored noise are modeled as autoregressive (AR) processes using speech and silence regions determined by an automatic end-point detector. Due to the non-stationary nature of the speech signal, non-stationary Kalman filter is used. Experiments indicate that non-stationary Kalman filtering outperforms the stationary case, the average SNR improvement increasing from 0.53 dB to 2.3 dB. Even better results are obtained if noise is considered also non-stationary, in addition to being colored, achieving an average of 7.14 dB SNR improvement.
['Dimitrie C. Popescu', 'Ilija Zeljkovic']
I am writing a research paper on a idea Kalman filtering of colored noise for speech enhancement
[{"from": "human", "value": "I am writing a research paper on a idea Kalman filtering of colored noise for speech enhancement"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A method for applying Kalman filtering to speech signals corrupted by colored noise is presented. Both speech and colored noise are modeled as autoregressive (AR) processes using speech and silence regions determined by an automatic end-point detector. Due to the non-stationary nature of the speech signal, non-stationary Kalman filter is used. Experiments indicate that non-stationary Kalman filtering outperforms the stationary case, the average SNR improvement increasing from 0.53 dB to 2.3 dB. Even better results are obtained if noise is considered also non-stationary, in addition to being colored, achieving an average of 7.14 dB SNR improvement."}]