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We show the initially segregated target and interferer in Figure 1d,e, respectively, and the final segregated target and interferer are presented in Figure 1f,g, respectively.
(d) Cochleagram of initially segregated target.
As shown in the figure, the segregated target loses more target energy (Figure 11a), but contains less interference as well (Figure 11b).
(f) Cochleagram of segregated target after iterative estimation.
In Figure 4, we measure the performance of mask estimation by the SNR gain of the segregated target.
This number drops to 0.7% at 15 dB SNR. Figure 11c shows the SNR of the segregated target.
Similar(51)
A lot of effort has been made in Computational Auditory Scene Analysis (CASA) to segregate target speech from monaural mixtures.
This algorithm first obtains a rough estimation of target pitch, and then uses this estimation to segregate target speech using harmonicity and temporal continuity.
(c) SNR of segregated voiced target.
In addition, the SNR of the segregated voiced target (in dB) provides a good comparison between waveforms [14]: S N R = 10 log 10 ∑ n s 2 n ∑ n s n - x ̃ n 2, (13).
If listeners perceptually segregated that target signal from the cosignal, no CMP would be seen.
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