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- W4319780101 abstract "Speaker extraction is to extract the target speaker from the mixture using a short piece of anchor speech. This paper introduces an improved target speaker extractor, referred to as Speakerfilter-Pro, based on previous Speakerfilter model. The Speakerfilter uses a bi-direction gated recurrent unit (BGRU) module to characterize the target speaker from anchor speech and uses a convolutional recurrent network (CRN) module to separate the target speech from a noisy signal. Different from the Speakerfilter, the Speakerfilter-Pro sticks a WaveUNet module in the beginning and the ending, respectively. The Wave-UNet has been proven to have a better ability to perform speech separation in the time domain. In order to extract the target speaker information better, the complex spectrum instead of the magnitude spectrum is utilized as the input feature for the CRN module. Experiments are conducted on the two-speaker dataset (WSJO-mix2) which is widely used for speaker extraction. The systematic evaluation shows that the Speakerfilter-Pro outperforms the Speakerfilter and other baselines, and achieves a signal-to-distortion ratio (SDR) of 14.95 dB." @default.
- W4319780101 created "2023-02-11" @default.
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- W4319780101 date "2022-12-11" @default.
- W4319780101 modified "2023-09-25" @default.
- W4319780101 title "Speakerfilter-Pro: an improved target speaker extractor combines the time domain and frequency domain" @default.
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- W4319780101 doi "https://doi.org/10.1109/iscslp57327.2022.10037794" @default.
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