Regularization for Partial Multichannel Equalization for Speech Dereverberation
Ina Kodrasi, Stefan Goetze, Simon Doclo
IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 9, pp. 1879-1890, Sep. 2013.
Acoustic multichannel equalization techniques such as the multiple-input/output inverse theorem (MINT), which aim to equalize the room impulse responses (RIRs) between the source and the microphone array, are known to be highly sensitive to RIR estimation errors. To increase robustness, it has been proposed to incorporate regularization in order to decrease the energy of the equalization filters. In addition, more robust partial multichannel equalization techniques such as relaxed multichannel least-squares (RMCLS) and channel shortening (CS) have recently been proposed.
In this paper, we propose a partial multichannel equalization technique based on MINT (P-MINT) which aims to shorten the RIR. Furthermore, we investigate the effectiveness of incorporating regularization to further increase the robustness of PMINT and the aforementioned partial multichannel equalization techniques, i.e. RMCLS and CS. In addition, we introduce an automatic non-intrusive procedure for determining the regularization parameter based on the L-curve.