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To model these time-delayed signals, a convoluted mixture is often used.
The mixing process of speech signals in our daily surroundings is modeled as a convoluted mixture [6].
Specifically, the short time Fourier transform (STFT) can convert a convoluted mixture in the time domain into instantaneous mixtures for all frequency bins.
Frequency-domain ISFA (FD-ISFA), which we proposed in our previous study [5], can handle a convoluted mixture that contains room reverberation.
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Since the convoluted mixture is converted into complex spectra by using STFT, the elements of X, S, A, and E become complex-valued variables.
This is because the convoluted mixture in the time domain can be explained in a simplistic form in the frequency domain.
As mentioned above, the convoluted mixture in the time domain is converted into instantaneous mixtures for individual frequency bins.
Attempts to solve a BSS problem involving convoluted mixtures of signals mainly use, frequency domain processing.
Since these methods are time-domain approaches, it is not appropriate for speech separation of convoluted mixtures.
Our method improves the average SIR by 2.82 0.41 dB compared with the baseline method based on FD-ISFA when separating convoluted mixtures of RT20 = 30 [ms] 600 [ms] room environments.
This is a convoluted case.
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CEO of Professional Science Editing for Scientists @ prosciediting.com