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학술지 Alias-and-Separate: Wideband Speech Coding Using Sub-Nyquist Sampling and Speech Separation
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저자
황수중, 이은균, 장인선, 신종원
발행일
202209
출처
IEEE Signal Processing Letters, v.29, pp.2003-2007
ISSN
1070-9908
출판사
IEEE
DOI
https://dx.doi.org/10.1109/LSP.2022.3207381
협약과제
22ZH1200, 초실감 입체공간 미디어·콘텐츠 원천기술연구, 이태진
초록
Decimation of a discrete-time signal below the Nyquist rate without applying an appropriate lowpass filter results in a distortion called aliasing. If wideband speech sampled at 16 kHz is decimated by 2 to result in a signal sampled at 8 kHz with aliasing, the decimated signal would be the summation of two speech-like signals, which are the narrowband speech covering 0-4 kHz and the spectrally flipped aliasing component coming from 8-4 kHz. Recently, the performance of speech separation has been remarkably improved with deep learning-based approaches, implying that the narrowband and aliasing components may be able to be separated. In this letter, we propose a novel method for low-rate wideband speech coding utilizing a standard narrowband codec. Instead of coding wideband speech using a wideband codec with a limited bitrate, we propose to decimate the input wideband speech incurring aliasing, and then encode it with a narrowband codec by allocating all the allowed bitrate to 0-4 kHz. After decoding the encoded bitstream, we apply a speech separation technique to obtain the narrowband and aliasing signals, which are then used to reconstruct the wideband speech by expansion, low/highpass filtering, and summation. Experimental results showed that the proposed method could achieve subjective quality comparable to the speeches coded by wideband codecs at higher bitrates in a subjective MUSHRA test.
KSP 제안 키워드
Learning-based, Low Pass Filter, Low-rate, Nyquist rate, Speech Separation, Speech coding, Subjective quality, deep learning(DL), discrete-time, high-pass filtering, novel method