Speech enhancement based on a microphone array and log-spectral amplitude estimation

We present a multi-microphone speech enhancement approach for minimizing the log-spectral amplitude (LSA) distortion in nonstationary noise environments. An adaptive beamformer with a generalized sidelobe canceller structure is applied to the noisy observed signals. In addition to the beamformer primary output, it provides reference noise signals by projecting the input signals onto the noise-only subspace. Presumably, a desired signal component is stronger at the beamformer output than at any reference noise signal, and a noise component is strongest at one of the reference signals. Hence, the ratio between the transient power at beamformer output and the transient power at the reference signals indicates whether such a transient is desired or interfering. Based on a Gaussian statistical model, and an appropriate decision-directed a priori SNR estimate, we derive an estimator for the signal presence probability. This estimator controls the rate of recursive averaging in obtaining a noise spectrum estimate by the minima controlled recursive averaging (MCRA) approach. Subsequently, spectral enhancement of the beamformer output is achieved by applying an optimal gain function, which minimizes the MSE of the log-spectra.