Multichannel Identification and Nonnegative Equalization for Dereverberation and Noise Reduction based on Convolutive Transfer Function - Archive ouverte HAL Access content directly
Journal Articles IEEE/ACM Transactions on Audio, Speech and Language Processing Year : 2018

Multichannel Identification and Nonnegative Equalization for Dereverberation and Noise Reduction based on Convolutive Transfer Function

(1) , (2) , (3) , (1)
1
2
3

Abstract

This paper addresses the problems of blind multichannel identification and equalization for joint speech dereverberation and noise reduction. The time-domain cross-relation method is hardly applicable for blind room impulse response identification due to the near-common zeros of the long impulse responses. We extend the cross-relation method 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 frequency response of the 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 identified complex-valued CTFs are not accurate enough for multichannel equalization due to the frequency aliasing of the CTFs. Thence, we only use the CTF magnitudes, which leads to a nonnegative multichannel equalization method based on a nonnegative convolution model between the STFT magnitude of the source signal and the CTF magnitude. Compared with the complex-valued convolution model, this nonnegative convolution model is shown to be more robust against the CTF perturbations. To recover the STFT magnitude of the source signal and to reduce the additive noise, the L2-norm fitting error between the STFT magnitude of the microphone signals and the nonnegative convolution is constrained to be less than a noise power related tolerance. Meanwhile, the L1-norm of the STFT magnitude of the source signal is minimized to impose the sparsity.
Fichier principal
Vignette du fichier
ctf_dereverberation.pdf (2.02 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01645749 , version 1 (23-11-2017)
hal-01645749 , version 2 (26-02-2018)
hal-01645749 , version 3 (14-05-2018)

Identifiers

Cite

Xiaofei Li, Sharon Gannot, Laurent Girin, Radu Horaud. Multichannel Identification and Nonnegative Equalization for Dereverberation and Noise Reduction based on Convolutive Transfer Function. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2018, 26 (10), pp.1755-1768. ⟨10.1109/TASLP.2018.2839362⟩. ⟨hal-01645749v3⟩
544 View
1263 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More