Section: New Results

Speech Dereverberation and Noise Reduction Using the Convolutive Transfer Function

We address the problems of blind multichannel identification and equalization for joint speech dereverberation and noise reduction. The standard time-domain cross-relation methods are hardly applicable for blind room impulse response identification due to the near-common zeros of the long impulse responses. We extend the cross-relation formulation to the short-time Fourier transform (STFT) domain, in which the time-domain impulse response is approximately represented by the convolutive transfer function (CTF) with much less coefficients. For the oversampled STFT, CTFs suffer from the common zeros caused by the non-flat-top STFT window. To overcome this, we propose to identify CTFs using the STFT framework with oversampled signals and critically sampled CTFs, which is a good trade-off between the frequency aliasing of the signals and the common zeros problem of CTFs. The phases of the identified CTFs are inaccurate due to the frequency aliasing of the CTFs, and thus only their magnitudes are used. This leads to a non-negative multichannel equalization method based on a non-negative convolution model between the STFT magnitude of the source signal and the CTF magnitude. To recover the STFT magnitude of the source signal and to reduce the additive noise, the ${\ell }_2$-norm fitting error between the STFT magnitude of the microphone signals and the non-negative convolution is constrained to be less than a noise power related tolerance. Meanwhile, the ${\ell }_1$-norm of the STFT magnitude of the source signal is minimized to impose the sparsity [38].

Website: https://team.inria.fr/perception/research/ctf-dereverberation/.