Multimicrophone Speech Dereverberation with Noise for Hands-free Communication

Reverberation is one of the primary factors that degrade the quality of speechsignals when recorded by a distant microphone in order to facilitate hands-freecommunication. Undoing the e®ect of reverberation is still a challenging problemespecially when additive noise and time-varying acoustic channels are considered. Inthis dissertation, several multimicrophone dereverberation techniques are developedthat can dereverberate the recorded speech as well as improve the signal-to-noiseratio (SNR) considering a practical acoustic environment. The methods are basedon the adaptive estimation of the long acoustic impulse responses (AIRs) using themultichannel LMS (MCLMS) algorithm. Although the MCLMS algorithm is attractivefor its simplicity and computational e±ciency, it su®ers from slow convergence rate,step-size ambiguity, and last but not the least, lack of robustness in the presenceof noise. A variable-step-size frequency-domain MCLMS algorithm is proposed thatcan ensure stability and optimal convergence speed both in the noise-free and noisyconditions. To improve the noise-robustness of the class of MCLMS algorithms, twonovel solutions, namely, excitation-driven MCLMS and spectrally constraint MCLMSalgorithms are proposed that can successfully estimate the long AIRs with reasonableaccuracy.Based on adaptive estimation of the AIRs, two di®erent dereverberation techniquesare proposed. In the ¯rst approach, dereverberation is achieved by suppressing thelate reverberation using channel shortening technique and the SNR is improved bydelay-and-sum beamforming. The proposed shortening algorithm is also optimizedso that it makes a trade-o® between shortening performance and spectral distortionin the dereverberated speech. In the second approach, the power of the speechcomponents in the received microphone signals are ¯rst enhanced by an eigen¯lterand then a block-adaptive zero-forcing equalizer is employed to eliminate the channeldistortion introduced by the AIRs and eigen¯lter. The eigen¯lter is e±ciently estimatedavoiding the tedious Cholesky factorization and it also resists spectral nulls so thatnoise ampli¯cation is mitigated at the output of the zero-forcing equalizer. Extensiveexperiments are conducted, using both simulated and real reverberant acousticchannels, which demonstrate that the proposed methods can o®er better speech qualityand SNR improvement as compared to the state-of-the-art dereverberation techniques.