Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A robust dereverberation method is presented for speech enhancement in a situation requiring adaptation where a speaker shifts his/her head under reverberant conditions causing the impulse responses to change frequently.  combine correlation-based blind deconvolution with modified spectral subtraction to improve the quality of inverse-filtered speech degraded by the estimation error of inverse filters obtained in practice. Our method computes inverse filters by using the correlation matrix beten input signals that can be observed without measuring room impulse responses. Inverse filtering reduces early reflection, which has most of the por of the reverberation, and then, spectral subtraction suppresses the tail of the inverse-filtered reverberation. The performance of our method in adaptation is demonstrated by experiments using measured room impulse responses. The subjective results indicated that this method provides superior speech quality to each of the individual methods: blind deconvolution and spectral subtraction.
["Ken'ichi Furuya", 'Akitoshi Kataoka']
I am writing a research paper on a idea Robust Speech Dereverberation Using Multichannel Blind Deconvolution With Spectral Subtraction
[{"from": "human", "value": "I am writing a research paper on a idea Robust Speech Dereverberation Using Multichannel Blind Deconvolution With Spectral Subtraction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A robust dereverberation method is presented for speech enhancement in a situation requiring adaptation where a speaker shifts his/her head under reverberant conditions causing the impulse responses to change frequently.  combine correlation-based blind deconvolution with modified spectral subtraction to improve the quality of inverse-filtered speech degraded by the estimation error of inverse filters obtained in practice. Our method computes inverse filters by using the correlation matrix beten input signals that can be observed without measuring room impulse responses. Inverse filtering reduces early reflection, which has most of the por of the reverberation, and then, spectral subtraction suppresses the tail of the inverse-filtered reverberation. The performance of our method in adaptation is demonstrated by experiments using measured room impulse responses. The subjective results indicated that this method provides superior speech quality to each of the individual methods: blind deconvolution and spectral subtraction."}]