Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a robust variant of nonnegative matrix factorization (NMF) based on complex Student's t distributions (t-NMF) for source separation of single-channel audio signals. The Itakura-Saito divergence NMF (Gaussian NMF) is justified for this purpose under an assumption that the complex spectra of source signals and those of the mixture signal are complex Gaussian distributed (the additiv-ity of por spectra holds). In fact, hover, the source spectra are often heavy-tailed distributed. When the source spectra are complex Cauchy distributed, for example, the mixture spectra are also complex Cauchy distributed (the additivity of amplitude spectra holds). Using the complex t distribution that includes the complex Gaussian and Cauchy distributions as its special cases,  propose t-NMF as a unified extension of Gaussian NMF and Cauchy NMF. Furthermore,  propose the corresponding variant of positive semidefinite tensor factorization based on multivariate complex t distributions (t-PSDTF). The experimental results shod that while t-NMF and t-PSDTF re comparative to Gaussian counterparts in terms of peak performance, they worked much better on average because they are insensitive to initialization and tend to avoid local optima.
['Kazuyoshi Yoshii', 'Katsutoshi Itoyama', 'Masataka Goto']
I am writing a research paper on a idea Student's T nonnegative matrix factorization and positive semidefinite tensor factorization for single-channel audio source separation
[{"from": "human", "value": "I am writing a research paper on a idea Student's T nonnegative matrix factorization and positive semidefinite tensor factorization for single-channel audio 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:  presents a robust variant of nonnegative matrix factorization (NMF) based on complex Student's t distributions (t-NMF) for source separation of single-channel audio signals. The Itakura-Saito divergence NMF (Gaussian NMF) is justified for this purpose under an assumption that the complex spectra of source signals and those of the mixture signal are complex Gaussian distributed (the additiv-ity of por spectra holds). In fact, hover, the source spectra are often heavy-tailed distributed. When the source spectra are complex Cauchy distributed, for example, the mixture spectra are also complex Cauchy distributed (the additivity of amplitude spectra holds). Using the complex t distribution that includes the complex Gaussian and Cauchy distributions as its special cases,  propose t-NMF as a unified extension of Gaussian NMF and Cauchy NMF. Furthermore,  propose the corresponding variant of positive semidefinite tensor factorization based on multivariate complex t distributions (t-PSDTF). The experimental results shod that while t-NMF and t-PSDTF re comparative to Gaussian counterparts in terms of peak performance, they worked much better on average because they are insensitive to initialization and tend to avoid local optima."}]