A Spatio–Temporal Speech Enhancement Technique Based on Generalized Eigenvalue Decomposition

We present a new spatio-temporal algorithm for speech enhancement using microphone arrays. Our technique uses an iterative method for computing the generalized eigenvectors of the multichannel data as measured from the microphone array. Coefficient adaptation is performed using the spatio-temporal correlation coefficient sequences of the observed data. The technique avoids large matrix-vector multiplications and has lower computational resource requirements as compared to competing methods. The technique also does not require a calibrated microphone array and is applicable to a wide variety of noise types, including stationary correlated noise and nonstationary speech-like (e.g., babble) background noise. Application of the method to microphone array data in various environmental settings indicate that the procedure can achieve significant gains in signal-to-interference ratios (SIRs) even in low SIR environments, without introducing musical tone artifacts in the enhanced speech.