Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A fast adaptive algorithm based on natural gradient is proposed in this paper to address the convergence issue in instantaneous blind source separation BSS problem in a noisy and non-stationary mixing scenario. The natural gradient technique overcomes slow convergence properties of the gradient adaptation technique when the slope of the cost function varies widely for small changes in the parameters. To speed up the convergence further  adopt one of the heuristic methods by incorporating a momentum term. Synthetically generated data as ll as real world data have been considered for validation purpose. Numerical experiments on sinusoidal signals and acoustic electromechanical signals confirm the superior performance of the proposed algorithm over the conventional natural gradient algorithm NGA in both noisy and noiseless situation as ll as in stationary and non-stationary mixing scenario.
['Niva Das', 'Binodini Tripathy', 'Srikanta Patnaik']
I am writing a research paper on a idea Adaptive fast algorithm based on natural gradient for instantaneous blind source separation
[{"from": "human", "value": "I am writing a research paper on a idea Adaptive fast algorithm based on natural gradient for instantaneous blind source separation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A fast adaptive algorithm based on natural gradient is proposed in this paper to address the convergence issue in instantaneous blind source separation BSS problem in a noisy and non-stationary mixing scenario. The natural gradient technique overcomes slow convergence properties of the gradient adaptation technique when the slope of the cost function varies widely for small changes in the parameters. To speed up the convergence further  adopt one of the heuristic methods by incorporating a momentum term. Synthetically generated data as ll as real world data have been considered for validation purpose. Numerical experiments on sinusoidal signals and acoustic electromechanical signals confirm the superior performance of the proposed algorithm over the conventional natural gradient algorithm NGA in both noisy and noiseless situation as ll as in stationary and non-stationary mixing scenario."}]