A Multi-Frame Approach to the Frequency-Domain Single-Channel Noise Reduction Problem
Yiteng Arden Huang, Jacob Benesty · IEEE Transactions on Audio Speech and Language Processing · 2011
This paper focuses on the class of single-channel noise reduction methods that are performed in the frequency domain via the short-time Fourier transform (STFT). The simplicity and relative effectiveness of this class of approaches make them the dominant choice in practical systems. Over the past years, many popular algorithms have been proposed. These algorithms, no matter how they are developed, have one feature in common: the solution is eventually formulated as a gain function applied to the STFT of the noisy signal only in the current frame, implying that the interframe correlation is ignored. This assumption is not accurate for speech enhancement since speech is a highly self-correlated signal. In this paper, by taking the interframe correlation into account, a new linear model for speech spectral estimation and some optimal filters are proposed. They include the multi-frame Wiener and minimum variance distortionless response (MVDR) filters. With these filters, both the narrowband and fullband signal-to-noise ratios (SNRs) can be improved. Furthermore, with the MVDR filter, speech distortion at the output can be zero. Simulations present promising results in support of the claimed merits obtained by theoretical analysis.