Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a method for estimating the nonlinearities which hold in post-nonlinear source separation. In particular and contrary to the state-of-art methods, our proposed approach uses a ak joint-sparsity sources assumption:  look for tiny temporal zones where only one source is active. This method is ll suited to non-stationary signals such as speech. The main novelty of our work consists of using nonlinear single-source confidence measures and curve clustering. Such an approach may be seen as an extension of linear instantaneous sparse component analysis to post-nonlinear mixtures. The performance of the approach is illustrated with some tests showing that the nonlinear functions are estimated accurately, with mean square errors around 4e–5 when the sources are “strongly” mixed.
['Matthieu Puigt', 'Anthony Griffin', 'Athanasios Mouchtaris']
I am writing a research paper on a idea Post-nonlinear speech mixture identification using single-source temporal zones & curve clustering
[{"from": "human", "value": "I am writing a research paper on a idea Post-nonlinear speech mixture identification using single-source temporal zones & curve clustering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a method for estimating the nonlinearities which hold in post-nonlinear source separation. In particular and contrary to the state-of-art methods, our proposed approach uses a ak joint-sparsity sources assumption:  look for tiny temporal zones where only one source is active. This method is ll suited to non-stationary signals such as speech. The main novelty of our work consists of using nonlinear single-source confidence measures and curve clustering. Such an approach may be seen as an extension of linear instantaneous sparse component analysis to post-nonlinear mixtures. The performance of the approach is illustrated with some tests showing that the nonlinear functions are estimated accurately, with mean square errors around 4e\u20135 when the sources are \u201cstrongly\u201d mixed."}]