Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel single-channel source separation method is presented to recover the original signals given only a single observed mixture in noisy environment. The proposed separation method is an online adaptive process and independent of parameters initialization. , a noisy pseudo-stereo mixing model is developed by formulating an artificial mixture from the observed mixture where the signals are modeled by the autoregressive process. The proposed demixing process composes of two steps: First, the noisy mixing model is enhanced by selecting the time-frequency (TF) units of signal presence and computing the mixture spectral amplitude, and second, an adaptive estimation of the parameters associated with each source is computed frame-by-frame, which is then used to construct a TF mask for the separation process. To assess the performance of the proposed method, noisy mixtures of real-audio sources with nonstationary noise have been conducted under various SNRs. Experiments show that the proposed algorithm has yielded superior separation performance especially in low input SNR compared with existing methods.
['Naruephorn Tengtrairat', 'Wai Lok Woo', 'Satnam Singh Dlay', 'Bin Gao']
I am writing a research paper on a idea Online Noisy Single-Channel Source Separation Using Adaptive Spectrum Amplitude Estimator and Masking
[{"from": "human", "value": "I am writing a research paper on a idea Online Noisy Single-Channel Source Separation Using Adaptive Spectrum Amplitude Estimator and Masking"}, {"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 single-channel source separation method is presented to recover the original signals given only a single observed mixture in noisy environment. The proposed separation method is an online adaptive process and independent of parameters initialization. , a noisy pseudo-stereo mixing model is developed by formulating an artificial mixture from the observed mixture where the signals are modeled by the autoregressive process. The proposed demixing process composes of two steps: First, the noisy mixing model is enhanced by selecting the time-frequency (TF) units of signal presence and computing the mixture spectral amplitude, and second, an adaptive estimation of the parameters associated with each source is computed frame-by-frame, which is then used to construct a TF mask for the separation process. To assess the performance of the proposed method, noisy mixtures of real-audio sources with nonstationary noise have been conducted under various SNRs. Experiments show that the proposed algorithm has yielded superior separation performance especially in low input SNR compared with existing methods."}]